{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The motive of this notebook is to test the trip based feature creation. Meaning, attributes like `elapsed_tm` and `outlier detection` that needs to implemented on the basis of trip, will be done through functions. These functions will then form part of a **module** and will be implemented on scale, to all the files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Created on = 27 Mar 2019\n",
    "\n",
    "Completed on = 27 March 2019\n",
    "\n",
    "Conclusion =  The Outlier detection and trip number and elapsed time columns were created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.notebook.set_autosave_interval(60000)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Autosaving every 60 seconds\n"
     ]
    }
   ],
   "source": [
    "%autosave 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "import statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (8, 8)\n",
    "pd.set_option(\"display.max_rows\", 200)\n",
    "pd.set_option(\"display.max_columns\", 200)\n",
    "sns.set_style(\"darkgrid\")\n",
    "os.chdir(\"/home/CWSHPMU2316/Desktop/EVRangePrediction/data/raw\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"358272088715191_2019-01-16_cb.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mo</th>\n",
       "      <th>tp</th>\n",
       "      <th>dr</th>\n",
       "      <th>ln</th>\n",
       "      <th>lt</th>\n",
       "      <th>hd</th>\n",
       "      <th>sp</th>\n",
       "      <th>tm</th>\n",
       "      <th>mn</th>\n",
       "      <th>mt</th>\n",
       "      <th>mh</th>\n",
       "      <th>ml</th>\n",
       "      <th>mr</th>\n",
       "      <th>SpeedLimit</th>\n",
       "      <th>hdop</th>\n",
       "      <th>numsat</th>\n",
       "      <th>IMEI</th>\n",
       "      <th>trip_id</th>\n",
       "      <th>EVTmg</th>\n",
       "      <th>EVVer</th>\n",
       "      <th>EVCfg</th>\n",
       "      <th>EVIGS</th>\n",
       "      <th>EVIGC</th>\n",
       "      <th>EVVSP</th>\n",
       "      <th>EVDRG</th>\n",
       "      <th>EVGPO</th>\n",
       "      <th>EVBRP</th>\n",
       "      <th>EVCFN</th>\n",
       "      <th>EVICR</th>\n",
       "      <th>EVTRQ</th>\n",
       "      <th>EVCST</th>\n",
       "      <th>EVDDC</th>\n",
       "      <th>EVBCA</th>\n",
       "      <th>EVBBV</th>\n",
       "      <th>EVDR1</th>\n",
       "      <th>EVDR2</th>\n",
       "      <th>EVRGT</th>\n",
       "      <th>EVACP</th>\n",
       "      <th>EVBAP_Latest</th>\n",
       "      <th>EVBAP_Max</th>\n",
       "      <th>EVBAP_Min</th>\n",
       "      <th>EVCCS</th>\n",
       "      <th>EVCM1</th>\n",
       "      <th>EVCTM</th>\n",
       "      <th>EVCCU</th>\n",
       "      <th>EVCSD</th>\n",
       "      <th>EVVCE</th>\n",
       "      <th>EVPSC_Latest</th>\n",
       "      <th>EVPSC_Max</th>\n",
       "      <th>EVPSC_Min</th>\n",
       "      <th>EVVOU</th>\n",
       "      <th>EVCOU</th>\n",
       "      <th>EVCPV</th>\n",
       "      <th>EVVCD</th>\n",
       "      <th>EVCCD</th>\n",
       "      <th>EVCSC</th>\n",
       "      <th>EVEST</th>\n",
       "      <th>EVCHS</th>\n",
       "      <th>EVR10</th>\n",
       "      <th>EVRMN</th>\n",
       "      <th>EVHVS</th>\n",
       "      <th>EVV12</th>\n",
       "      <th>EVPWA_MAX</th>\n",
       "      <th>EVPWA_MIN</th>\n",
       "      <th>EVMCV_MAX</th>\n",
       "      <th>EVMCV_MIN</th>\n",
       "      <th>EVSMA_MAX</th>\n",
       "      <th>EVSMA_MIN</th>\n",
       "      <th>EVSMI_MAX</th>\n",
       "      <th>EVSMI_MIN</th>\n",
       "      <th>EVSOH</th>\n",
       "      <th>EVBMA_Latest</th>\n",
       "      <th>EVBMA_Max</th>\n",
       "      <th>EVBMA_Min</th>\n",
       "      <th>EVBMI_Latest</th>\n",
       "      <th>EVBMI_Max</th>\n",
       "      <th>EVBMI_Min</th>\n",
       "      <th>EVBOA_AVG</th>\n",
       "      <th>EVBOA_MAX</th>\n",
       "      <th>EVBOA_MIN</th>\n",
       "      <th>EVBOV_AVG</th>\n",
       "      <th>EVBOV_MAX</th>\n",
       "      <th>EVBOV_MIN</th>\n",
       "      <th>EVIRP</th>\n",
       "      <th>EVIRN</th>\n",
       "      <th>EVSOMA</th>\n",
       "      <th>EVSOMI</th>\n",
       "      <th>EVIGM_Latest</th>\n",
       "      <th>EVIGM_Max</th>\n",
       "      <th>EVIGM_Min</th>\n",
       "      <th>EVCOM_Latest</th>\n",
       "      <th>EVCOM_Max</th>\n",
       "      <th>EVCOM_Min</th>\n",
       "      <th>EVICO_Latest</th>\n",
       "      <th>EVICO_Max</th>\n",
       "      <th>EVICO_Min</th>\n",
       "      <th>EVIRT_Latest</th>\n",
       "      <th>EVIRT_Max</th>\n",
       "      <th>EVIRT_Min</th>\n",
       "      <th>EVIDC</th>\n",
       "      <th>EVMGT</th>\n",
       "      <th>EVMGS</th>\n",
       "      <th>EVMGF</th>\n",
       "      <th>EVMGR</th>\n",
       "      <th>EVIND</th>\n",
       "      <th>EVICM</th>\n",
       "      <th>EVCPW</th>\n",
       "      <th>EVCPF_Latest</th>\n",
       "      <th>EVCPF_Max</th>\n",
       "      <th>EVCPF_Min</th>\n",
       "      <th>EVCI1_Latest</th>\n",
       "      <th>EVCI1_Max</th>\n",
       "      <th>EVCI1_Min</th>\n",
       "      <th>EVCI2_Latest</th>\n",
       "      <th>EVCI2_Max</th>\n",
       "      <th>EVCI2_Min</th>\n",
       "      <th>EVCBD_Latest</th>\n",
       "      <th>EVCBD_Max</th>\n",
       "      <th>EVCBD_Min</th>\n",
       "      <th>EVCRP</th>\n",
       "      <th>EVACV_AVG</th>\n",
       "      <th>EVACV_Max</th>\n",
       "      <th>EVACV_Min</th>\n",
       "      <th>EVCDO_AVG</th>\n",
       "      <th>EVCDO_Max</th>\n",
       "      <th>EVCDO_Min</th>\n",
       "      <th>EVCCO_AVG</th>\n",
       "      <th>EVCCO_Max</th>\n",
       "      <th>EVCCO_Min</th>\n",
       "      <th>EVSDT</th>\n",
       "      <th>EVCHC</th>\n",
       "      <th>EVCHT_AVG</th>\n",
       "      <th>EVCHT_Max</th>\n",
       "      <th>EVCHT_Min</th>\n",
       "      <th>EVCHE</th>\n",
       "      <th>EVDI1</th>\n",
       "      <th>EVDI2</th>\n",
       "      <th>EVDIT</th>\n",
       "      <th>EVOII</th>\n",
       "      <th>EVDOA</th>\n",
       "      <th>EVDOV</th>\n",
       "      <th>EVDSE</th>\n",
       "      <th>EVACS</th>\n",
       "      <th>EVPSS</th>\n",
       "      <th>EVMSC</th>\n",
       "      <th>EVRER</th>\n",
       "      <th>EVDRV</th>\n",
       "      <th>EVMTR</th>\n",
       "      <th>EVODO</th>\n",
       "      <th>EVOAS</th>\n",
       "      <th>EVHTR</th>\n",
       "      <th>EVACE</th>\n",
       "      <th>EVTRE</th>\n",
       "      <th>EVCLC</th>\n",
       "      <th>EVBFN</th>\n",
       "      <th>EVIST</th>\n",
       "      <th>EVHTP_AVG</th>\n",
       "      <th>EVHTP_Max</th>\n",
       "      <th>EVHTP_Min</th>\n",
       "      <th>EVVBT</th>\n",
       "      <th>EVGSM</th>\n",
       "      <th>EVACO_X</th>\n",
       "      <th>EVACO_Y</th>\n",
       "      <th>EVACO_Z</th>\n",
       "      <th>Unnamed: 164</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>76.951081</td>\n",
       "      <td>28.390039</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1547605702400</td>\n",
       "      <td>76.951081</td>\n",
       "      <td>28.390039</td>\n",
       "      <td>87.78603</td>\n",
       "      <td>1036056339</td>\n",
       "      <td>5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>7</td>\n",
       "      <td>358272088715191</td>\n",
       "      <td>Trip not started</td>\n",
       "      <td>P</td>\n",
       "      <td>M1_POCEV.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>246</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>60.5</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.88</td>\n",
       "      <td>78.4</td>\n",
       "      <td>78.4</td>\n",
       "      <td>78.1</td>\n",
       "      <td>78.1</td>\n",
       "      <td>100</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-505.0</td>\n",
       "      <td>-357.0</td>\n",
       "      <td>257.75</td>\n",
       "      <td>174.0</td>\n",
       "      <td>16180.0</td>\n",
       "      <td>1790</td>\n",
       "      <td>1790</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>50.0</td>\n",
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       "      <td>28.390039</td>\n",
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       "      <td>1547605702500</td>\n",
       "      <td>76.951081</td>\n",
       "      <td>28.390039</td>\n",
       "      <td>87.78603</td>\n",
       "      <td>1036056339</td>\n",
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       "      <td>30.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>7</td>\n",
       "      <td>358272088715191</td>\n",
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       "      <td>3.88</td>\n",
       "      <td>78.4</td>\n",
       "      <td>78.4</td>\n",
       "      <td>78.1</td>\n",
       "      <td>78.1</td>\n",
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       "      <td>15.5</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.225</td>\n",
       "      <td>52.9</td>\n",
       "      <td>0</td>\n",
       "      <td>-65532</td>\n",
       "      <td>0</td>\n",
       "      <td>2572</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.7</td>\n",
       "      <td>50.0</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "      <td>-820</td>\n",
       "      <td>654</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DEFREG:358272088715191</td>\n",
       "      <td>Trip not started</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.951081</td>\n",
       "      <td>28.390039</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1547605702600</td>\n",
       "      <td>76.951081</td>\n",
       "      <td>28.390039</td>\n",
       "      <td>87.78603</td>\n",
       "      <td>1036056339</td>\n",
       "      <td>5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>7</td>\n",
       "      <td>358272088715191</td>\n",
       "      <td>Trip not started</td>\n",
       "      <td>P</td>\n",
       "      <td>M1_POCEV.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>246</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>60.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>13.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.88</td>\n",
       "      <td>78.4</td>\n",
       "      <td>78.4</td>\n",
       "      <td>78.1</td>\n",
       "      <td>78.1</td>\n",
       "      <td>100</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-505.0</td>\n",
       "      <td>-357.0</td>\n",
       "      <td>257.75</td>\n",
       "      <td>174.0</td>\n",
       "      <td>16180.0</td>\n",
       "      <td>1790</td>\n",
       "      <td>1790</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>14</td>\n",
       "      <td>-50</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>106</td>\n",
       "      <td>202</td>\n",
       "      <td>15</td>\n",
       "      <td>-50</td>\n",
       "      <td>-16</td>\n",
       "      <td>14</td>\n",
       "      <td>88</td>\n",
       "      <td>212</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>260</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.225</td>\n",
       "      <td>52.9</td>\n",
       "      <td>0</td>\n",
       "      <td>-65532</td>\n",
       "      <td>0</td>\n",
       "      <td>2572</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.7</td>\n",
       "      <td>50.0</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "      <td>-820</td>\n",
       "      <td>654</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DEFREG:358272088715191</td>\n",
       "      <td>Trip not started</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.951081</td>\n",
       "      <td>28.390039</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1547605702700</td>\n",
       "      <td>76.951081</td>\n",
       "      <td>28.390039</td>\n",
       "      <td>87.78603</td>\n",
       "      <td>1036056339</td>\n",
       "      <td>5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>7</td>\n",
       "      <td>358272088715191</td>\n",
       "      <td>Trip not started</td>\n",
       "      <td>P</td>\n",
       "      <td>M1_POCEV.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>246</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>60.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>13.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.88</td>\n",
       "      <td>78.4</td>\n",
       "      <td>78.4</td>\n",
       "      <td>78.1</td>\n",
       "      <td>78.1</td>\n",
       "      <td>100</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-505.0</td>\n",
       "      <td>-357.0</td>\n",
       "      <td>257.75</td>\n",
       "      <td>174.0</td>\n",
       "      <td>16180.0</td>\n",
       "      <td>1790</td>\n",
       "      <td>1790</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>14</td>\n",
       "      <td>-50</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>106</td>\n",
       "      <td>202</td>\n",
       "      <td>15</td>\n",
       "      <td>-50</td>\n",
       "      <td>-16</td>\n",
       "      <td>14</td>\n",
       "      <td>88</td>\n",
       "      <td>212</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>260</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.225</td>\n",
       "      <td>52.9</td>\n",
       "      <td>0</td>\n",
       "      <td>-65532</td>\n",
       "      <td>0</td>\n",
       "      <td>2572</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.7</td>\n",
       "      <td>50.0</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "      <td>-820</td>\n",
       "      <td>654</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DEFREG:358272088715191</td>\n",
       "      <td>Trip not started</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.951081</td>\n",
       "      <td>28.390039</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1547605702800</td>\n",
       "      <td>76.951081</td>\n",
       "      <td>28.390039</td>\n",
       "      <td>87.78603</td>\n",
       "      <td>1036056339</td>\n",
       "      <td>5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>7</td>\n",
       "      <td>358272088715191</td>\n",
       "      <td>Trip not started</td>\n",
       "      <td>P</td>\n",
       "      <td>M1_POCEV.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>246</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>60.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>13.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.88</td>\n",
       "      <td>78.4</td>\n",
       "      <td>78.4</td>\n",
       "      <td>78.1</td>\n",
       "      <td>78.1</td>\n",
       "      <td>100</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-505.0</td>\n",
       "      <td>-357.0</td>\n",
       "      <td>257.75</td>\n",
       "      <td>174.0</td>\n",
       "      <td>16180.0</td>\n",
       "      <td>1790</td>\n",
       "      <td>1790</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>14</td>\n",
       "      <td>-50</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>106</td>\n",
       "      <td>202</td>\n",
       "      <td>15</td>\n",
       "      <td>-50</td>\n",
       "      <td>-16</td>\n",
       "      <td>14</td>\n",
       "      <td>88</td>\n",
       "      <td>212</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>260</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.225</td>\n",
       "      <td>52.9</td>\n",
       "      <td>0</td>\n",
       "      <td>-65532</td>\n",
       "      <td>0</td>\n",
       "      <td>2572</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.7</td>\n",
       "      <td>50.0</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "      <td>-820</td>\n",
       "      <td>654</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       mo                tp  dr         ln         lt   hd  \\\n",
       "0  DEFREG:358272088715191  Trip not started NaN  76.951081  28.390039  1.0   \n",
       "1  DEFREG:358272088715191  Trip not started NaN  76.951081  28.390039  1.0   \n",
       "2  DEFREG:358272088715191  Trip not started NaN  76.951081  28.390039  1.0   \n",
       "3  DEFREG:358272088715191  Trip not started NaN  76.951081  28.390039  1.0   \n",
       "4  DEFREG:358272088715191  Trip not started NaN  76.951081  28.390039  1.0   \n",
       "\n",
       "   sp             tm         mn         mt        mh          ml  mr  \\\n",
       "0 NaN  1547605702400  76.951081  28.390039  87.78603  1036056339   5   \n",
       "1 NaN  1547605702500  76.951081  28.390039  87.78603  1036056339   5   \n",
       "2 NaN  1547605702600  76.951081  28.390039  87.78603  1036056339   5   \n",
       "3 NaN  1547605702700  76.951081  28.390039  87.78603  1036056339   5   \n",
       "4 NaN  1547605702800  76.951081  28.390039  87.78603  1036056339   5   \n",
       "\n",
       "   SpeedLimit  hdop  numsat             IMEI           trip_id EVTmg  \\\n",
       "0        30.0   1.8       7  358272088715191  Trip not started     P   \n",
       "1        30.0   1.8       7  358272088715191  Trip not started     P   \n",
       "2        30.0   1.8       7  358272088715191  Trip not started     P   \n",
       "3        30.0   1.8       7  358272088715191  Trip not started     P   \n",
       "4        30.0   1.8       7  358272088715191  Trip not started     P   \n",
       "\n",
       "        EVVer  EVCfg  EVIGS  EVIGC  EVVSP  EVDRG  EVGPO  EVBRP  EVCFN  EVICR  \\\n",
       "0  M1_POCEV.0    NaN      1    246    0.0     87     10      0      0      1   \n",
       "1  M1_POCEV.0    NaN      1    246    0.0     87     10      0      0      1   \n",
       "2  M1_POCEV.0    NaN      1    246    0.0     87     10      0      0      1   \n",
       "3  M1_POCEV.0    NaN      1    246    0.0     87     10      0      0      1   \n",
       "4  M1_POCEV.0    NaN      1    246    0.0     87     10      0      0      1   \n",
       "\n",
       "   EVTRQ  EVCST  EVDDC  EVBCA  EVBBV  EVDR1  EVDR2  EVRGT  EVACP  \\\n",
       "0    0.0      1   13.9      0   60.5      0      0      0    0.0   \n",
       "1    0.0      1   13.9      0   60.5      0      0      0    0.0   \n",
       "2    0.0      1   13.9      0   60.5      0      0      0    0.0   \n",
       "3    0.0      1   13.9      0   60.5      0      0      0    0.0   \n",
       "4    0.0      1   13.9      0   60.5      0      0      0    0.0   \n",
       "\n",
       "   EVBAP_Latest  EVBAP_Max  EVBAP_Min  EVCCS  EVCM1  EVCTM  EVCCU  EVCSD  \\\n",
       "0             0          0          0      0    NaN    NaN    NaN    NaN   \n",
       "1             0          0          0      0    NaN    NaN    NaN    NaN   \n",
       "2             0          0          0      0    NaN    NaN    NaN    NaN   \n",
       "3             0          0          0      0    NaN    NaN    NaN    NaN   \n",
       "4             0          0          0      0    NaN    NaN    NaN    NaN   \n",
       "\n",
       "   EVVCE  EVPSC_Latest  EVPSC_Max  EVPSC_Min  EVVOU  EVCOU  EVCPV  EVVCD  \\\n",
       "0    NaN           NaN        NaN        NaN    NaN    NaN    NaN    NaN   \n",
       "1    NaN           NaN        NaN        NaN    NaN    NaN    NaN    NaN   \n",
       "2    NaN           NaN        NaN        NaN    NaN    NaN    NaN    NaN   \n",
       "3    NaN           NaN        NaN        NaN    NaN    NaN    NaN    NaN   \n",
       "4    NaN           NaN        NaN        NaN    NaN    NaN    NaN    NaN   \n",
       "\n",
       "   EVCCD  EVCSC  EVEST  EVCHS  EVR10  EVRMN  EVHVS  EVV12  EVPWA_MAX  \\\n",
       "0    NaN    NaN    NaN    NaN    NaN    NaN     10   13.8          0   \n",
       "1    NaN    NaN    NaN    NaN    NaN    NaN     10   13.8          0   \n",
       "2    NaN    NaN    NaN    NaN    NaN    NaN     10   13.8          0   \n",
       "3    NaN    NaN    NaN    NaN    NaN    NaN     10   13.8          0   \n",
       "4    NaN    NaN    NaN    NaN    NaN    NaN     10   13.8          0   \n",
       "\n",
       "   EVPWA_MIN  EVMCV_MAX  EVMCV_MIN  EVSMA_MAX  EVSMA_MIN  EVSMI_MAX  \\\n",
       "0          0       3.89       3.88       78.4       78.4       78.1   \n",
       "1          0       3.89       3.88       78.4       78.4       78.1   \n",
       "2          0       3.89       3.88       78.4       78.4       78.1   \n",
       "3          0       3.89       3.88       78.4       78.4       78.1   \n",
       "4          0       3.89       3.88       78.4       78.4       78.1   \n",
       "\n",
       "   EVSMI_MIN  EVSOH  EVBMA_Latest  EVBMA_Max  EVBMA_Min  EVBMI_Latest  \\\n",
       "0       78.1    100          15.5       15.5       15.5          15.0   \n",
       "1       78.1    100          15.5       15.5       15.5          15.0   \n",
       "2       78.1    100          15.5       15.5       15.5          15.0   \n",
       "3       78.1    100          15.5       15.5       15.5          15.0   \n",
       "4       78.1    100          15.5       15.5       15.5          15.0   \n",
       "\n",
       "   EVBMI_Max  EVBMI_Min  EVBOA_AVG  EVBOA_MAX  EVBOA_MIN  EVBOV_AVG  \\\n",
       "0       15.0       15.0        0.5     -505.0     -357.0     257.75   \n",
       "1       15.0       15.0        0.5     -505.0     -357.0     257.75   \n",
       "2       15.0       15.0        0.5     -505.0     -357.0     257.75   \n",
       "3       15.0       15.0        0.5     -505.0     -357.0     257.75   \n",
       "4       15.0       15.0        0.5     -505.0     -357.0     257.75   \n",
       "\n",
       "   EVBOV_MAX  EVBOV_MIN  EVIRP  EVIRN  EVSOMA  EVSOMI  EVIGM_Latest  \\\n",
       "0      174.0    16180.0   1790   1790     100     100            14   \n",
       "1      174.0    16180.0   1790   1790     100     100            14   \n",
       "2      174.0    16180.0   1790   1790     100     100            14   \n",
       "3      174.0    16180.0   1790   1790     100     100            14   \n",
       "4      174.0    16180.0   1790   1790     100     100            14   \n",
       "\n",
       "   EVIGM_Max  EVIGM_Min  EVCOM_Latest  EVCOM_Max  EVCOM_Min  EVICO_Latest  \\\n",
       "0        -50          2            14        106        202            15   \n",
       "1        -50          2            14        106        202            15   \n",
       "2        -50          2            14        106        202            15   \n",
       "3        -50          2            14        106        202            15   \n",
       "4        -50          2            14        106        202            15   \n",
       "\n",
       "   EVICO_Max  EVICO_Min  EVIRT_Latest  EVIRT_Max  EVIRT_Min  EVIDC  EVMGT  \\\n",
       "0        -50        -16            14         88        212    0.0   -0.1   \n",
       "1        -50        -16            14         88        212    0.0   -0.1   \n",
       "2        -50        -16            14         88        212    0.0   -0.1   \n",
       "3        -50        -16            14         88        212    0.0   -0.1   \n",
       "4        -50        -16            14         88        212    0.0   -0.1   \n",
       "\n",
       "   EVMGS  EVMGF  EVMGR  EVIND  EVICM  EVCPW  EVCPF_Latest  EVCPF_Max  \\\n",
       "0      0    0.0    0.0    260      3    NaN           NaN        NaN   \n",
       "1      0    0.0    0.0    260      3    NaN           NaN        NaN   \n",
       "2      0    0.0    0.0    260      3    NaN           NaN        NaN   \n",
       "3      0    0.0    0.0    260      3    NaN           NaN        NaN   \n",
       "4      0    0.0    0.0    260      3    NaN           NaN        NaN   \n",
       "\n",
       "   EVCPF_Min  EVCI1_Latest  EVCI1_Max  EVCI1_Min  EVCI2_Latest  EVCI2_Max  \\\n",
       "0        NaN           NaN        NaN        NaN           NaN        NaN   \n",
       "1        NaN           NaN        NaN        NaN           NaN        NaN   \n",
       "2        NaN           NaN        NaN        NaN           NaN        NaN   \n",
       "3        NaN           NaN        NaN        NaN           NaN        NaN   \n",
       "4        NaN           NaN        NaN        NaN           NaN        NaN   \n",
       "\n",
       "   EVCI2_Min  EVCBD_Latest  EVCBD_Max  EVCBD_Min  EVCRP  EVACV_AVG  EVACV_Max  \\\n",
       "0        NaN           NaN        NaN        NaN      1        NaN        NaN   \n",
       "1        NaN           NaN        NaN        NaN      1        NaN        NaN   \n",
       "2        NaN           NaN        NaN        NaN      1        NaN        NaN   \n",
       "3        NaN           NaN        NaN        NaN      1        NaN        NaN   \n",
       "4        NaN           NaN        NaN        NaN      1        NaN        NaN   \n",
       "\n",
       "   EVACV_Min  EVCDO_AVG  EVCDO_Max  EVCDO_Min  EVCCO_AVG  EVCCO_Max  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   EVCCO_Min  EVSDT  EVCHC  EVCHT_AVG  EVCHT_Max  EVCHT_Min  EVCHE  EVDI1  \\\n",
       "0        NaN    NaN    NaN        NaN        NaN        NaN    NaN     15   \n",
       "1        NaN    NaN    NaN        NaN        NaN        NaN    NaN     15   \n",
       "2        NaN    NaN    NaN        NaN        NaN        NaN    NaN     15   \n",
       "3        NaN    NaN    NaN        NaN        NaN        NaN    NaN     15   \n",
       "4        NaN    NaN    NaN        NaN        NaN        NaN    NaN     15   \n",
       "\n",
       "   EVDI2  EVDIT  EVOII  EVDOA  EVDOV  EVDSE  EVACS   EVPSS  EVMSC  EVRER  \\\n",
       "0     14     14      0     27   13.9      0    0.0  46.225   52.9      0   \n",
       "1     14     14      0     27   13.9      0    0.0  46.225   52.9      0   \n",
       "2     14     14      0     27   13.9      0    0.0  46.225   52.9      0   \n",
       "3     14     14      0     27   13.9      0    0.0  46.225   52.9      0   \n",
       "4     14     14      0     27   13.9      0    0.0  46.225   52.9      0   \n",
       "\n",
       "   EVDRV  EVMTR  EVODO  EVOAS  EVHTR  EVACE  EVTRE  EVCLC  EVBFN  EVIST  \\\n",
       "0 -65532      0   2572   13.0      0   13.7   50.0     55      0      0   \n",
       "1 -65532      0   2572   13.0      0   13.7   50.0     55      0      0   \n",
       "2 -65532      0   2572   13.0      0   13.7   50.0     55      0      0   \n",
       "3 -65532      0   2572   13.0      0   13.7   50.0     55      0      0   \n",
       "4 -65532      0   2572   13.0      0   13.7   50.0     55      0      0   \n",
       "\n",
       "   EVHTP_AVG  EVHTP_Max  EVHTP_Min  EVVBT  EVGSM  EVACO_X  EVACO_Y  EVACO_Z  \\\n",
       "0          0          0          0    137      0     -820      654       23   \n",
       "1          0          0          0    137      0     -820      654       23   \n",
       "2          0          0          0    137      0     -820      654       23   \n",
       "3          0          0          0    137      0     -820      654       23   \n",
       "4          0          0          0    137      0     -820      654       23   \n",
       "\n",
       "   Unnamed: 164  \n",
       "0           NaN  \n",
       "1           NaN  \n",
       "2           NaN  \n",
       "3           NaN  \n",
       "4           NaN  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trip Number\n",
    "Filling the column with `tp` with the current trip vehicle is in. This will allow us `group_by` the observation based on trip number and perform trip specific feature engineering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trip_number(dataframe):\n",
    "    \"\"\"\n",
    "    input - dataframe\n",
    "    output - list\n",
    "    \n",
    "    finding out the index across the data, wherever new trip starts\n",
    "    Logic used - whenever the value of EVIGC changes AND difference b/w two consecutive timestamps is greater than 100, \n",
    "    new trip starts.\n",
    "    \"\"\"\n",
    "    new_trip = []                  # initializing the list\n",
    "    new_trip.append(data.index[0]) # appending the first value as it signify the first trip\n",
    "    for i in range(1, len(data)):\n",
    "        if (data.EVIGC[i] - data.EVIGC[i-1] >= 1) and (data.tm[i] - data.tm[i-1]) > 100:\n",
    "            new_trip.append(data.index[i])\n",
    "            \n",
    "    # the terminating value of the dataset. This value will be used as upper limit for last trip        \n",
    "    new_trip.append(len(data))     \n",
    "    \n",
    "    # making a list that fills that repeatedly fills the trip number for the length of data we have\n",
    "    trip_number_list = []                \n",
    "    for k in range(0, len(new_trip)-1):\n",
    "        trip_number = k+1\n",
    "        lower_limit = new_trip[k]\n",
    "        upper_limit = new_trip[k+1]\n",
    "        for i in range(lower_limit, upper_limit):\n",
    "            trip_number_list.append(trip_number)\n",
    "            \n",
    "    return trip_number_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def elapsed_time(dataframe):\n",
    "    \"\"\"\n",
    "    input - dataframe\n",
    "    output - list\n",
    "    this function takes the dataframe and outputs the elapsed time list.\n",
    "    logic = current timestamp - start time stamp (where trip starts)\n",
    "    \"\"\"\n",
    "    elapsed_tm = []\n",
    "    new_trip = []                  # initializing the list\n",
    "    new_trip.append(data.index[0]) # appending the first value as it signify the first trip\n",
    "\n",
    "    for i in range(1, len(data)):\n",
    "        if (data.EVIGC[i] - data.EVIGC[i-1] >= 1) and (data.tm[i] - data.tm[i-1]) > 100:\n",
    "            new_trip.append(data.index[i])\n",
    "    new_trip.append(len(data))\n",
    "\n",
    "    for i in range(0, len(new_trip) - 1):\n",
    "        init_trip = new_trip[i]\n",
    "        stop_trip = new_trip[i+1]\n",
    "        for k in range(init_trip, stop_trip):\n",
    "            temp = data.tm[k] - data.tm[init_trip]\n",
    "            elapsed_tm.append(temp)\n",
    "            \n",
    "    return elapsed_tm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_df(dataframe, attribute_name, list_name):\n",
    "    \"\"\"\n",
    "    inputs - dataframe name, list name (should be of same length), name of the predictor to be replaced\n",
    "    output - dataframe with replaced column\n",
    "    \"\"\"\n",
    "    col_list = list(dataframe.columns)\n",
    "    loc = col_list.index(attribute_name)\n",
    "    dataframe = dataframe.drop(columns = attribute_name)\n",
    "    dataframe.insert(loc, attribute_name, list_name)\n",
    "    return dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "trip_number_list = trip_number(data)\n",
    "elapsed_list = elapsed_time(data)\n",
    "\n",
    "df = replace_df(data, \"tp\", trip_number_list)\n",
    "df = replace_df(df, \"tm\", elapsed_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def outlier_imputation(dataframe, window_size):\n",
    "    \"\"\"\n",
    "    input - dataframe\n",
    "    output - dataframe with outlier imputation\n",
    "    \"\"\"\n",
    "    outlier_dictionary = {\n",
    "        \"EVTRQ\": [-200, 200],\n",
    "        \"EVDDC\": [12.5, 15.6],\n",
    "        \"EVRGT\": [-200, 200],\n",
    "        \"EVBOA_MIN\": [-100,512],\n",
    "        \"EVBOA_MAX\": [-100, 512],\n",
    "        \"EVBOA_AVG\": [-100, 512],\n",
    "        \"EVBOV_MIN\": [0, 512],\n",
    "        \"EVBOV_MAX\": [0, 512],\n",
    "        \"EVBOV_AVG\": [0, 512],\n",
    "        \"EVPWA_MAX\": [0,12.7],\n",
    "        \"EVBMA_Latest\" : [-40,86.5],\n",
    "        \"EVIRP\": [0,4000] \n",
    "    }\n",
    "    \n",
    "    for i in outlier_dictionary:\n",
    "        arr = dataframe[i].values\n",
    "        new_list = arr[:window_size]\n",
    "        new_list = list(new_list)\n",
    "        \n",
    "        lower_limit = outlier_dictionary[i][0]\n",
    "        upper_limit = outlier_dictionary[i][1]\n",
    "        for j in range(window_size, len(arr)):\n",
    "            w_data = arr[(j - window_size): j]\n",
    "            median = statistics.median(w_data)\n",
    "            \n",
    "            \n",
    "            if w_data[-1] < lower_limit or w_data[-1] > upper_limit:\n",
    "                new_list.append(median)\n",
    "\n",
    "            else:\n",
    "                new_list.append(w_data[-1])\n",
    "            \n",
    "        for k in range(0, window_size):\n",
    "            if new_list[k]< lower_limit or new_list[k]>upper_limit:\n",
    "                new_list[k] = statistics.median(arr)\n",
    "                \n",
    "        dataframe = replace_df(dataframe, i, new_list)\n",
    "    \n",
    "    return dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mo</th>\n",
       "      <th>tp</th>\n",
       "      <th>dr</th>\n",
       "      <th>ln</th>\n",
       "      <th>lt</th>\n",
       "      <th>hd</th>\n",
       "      <th>sp</th>\n",
       "      <th>tm</th>\n",
       "      <th>mn</th>\n",
       "      <th>mt</th>\n",
       "      <th>mh</th>\n",
       "      <th>ml</th>\n",
       "      <th>mr</th>\n",
       "      <th>SpeedLimit</th>\n",
       "      <th>hdop</th>\n",
       "      <th>numsat</th>\n",
       "      <th>IMEI</th>\n",
       "      <th>trip_id</th>\n",
       "      <th>EVTmg</th>\n",
       "      <th>EVVer</th>\n",
       "      <th>EVCfg</th>\n",
       "      <th>EVIGS</th>\n",
       "      <th>EVIGC</th>\n",
       "      <th>EVVSP</th>\n",
       "      <th>EVDRG</th>\n",
       "      <th>EVGPO</th>\n",
       "      <th>EVBRP</th>\n",
       "      <th>EVCFN</th>\n",
       "      <th>EVICR</th>\n",
       "      <th>EVTRQ</th>\n",
       "      <th>EVCST</th>\n",
       "      <th>EVDDC</th>\n",
       "      <th>EVBCA</th>\n",
       "      <th>EVBBV</th>\n",
       "      <th>EVDR1</th>\n",
       "      <th>EVDR2</th>\n",
       "      <th>EVRGT</th>\n",
       "      <th>EVACP</th>\n",
       "      <th>EVBAP_Latest</th>\n",
       "      <th>EVBAP_Max</th>\n",
       "      <th>EVBAP_Min</th>\n",
       "      <th>EVCCS</th>\n",
       "      <th>EVCM1</th>\n",
       "      <th>EVCTM</th>\n",
       "      <th>EVCCU</th>\n",
       "      <th>EVCSD</th>\n",
       "      <th>EVVCE</th>\n",
       "      <th>EVPSC_Latest</th>\n",
       "      <th>EVPSC_Max</th>\n",
       "      <th>EVPSC_Min</th>\n",
       "      <th>EVVOU</th>\n",
       "      <th>EVCOU</th>\n",
       "      <th>EVCPV</th>\n",
       "      <th>EVVCD</th>\n",
       "      <th>EVCCD</th>\n",
       "      <th>EVCSC</th>\n",
       "      <th>EVEST</th>\n",
       "      <th>EVCHS</th>\n",
       "      <th>EVR10</th>\n",
       "      <th>EVRMN</th>\n",
       "      <th>EVHVS</th>\n",
       "      <th>EVV12</th>\n",
       "      <th>EVPWA_MAX</th>\n",
       "      <th>EVPWA_MIN</th>\n",
       "      <th>EVMCV_MAX</th>\n",
       "      <th>EVMCV_MIN</th>\n",
       "      <th>EVSMA_MAX</th>\n",
       "      <th>EVSMA_MIN</th>\n",
       "      <th>EVSMI_MAX</th>\n",
       "      <th>EVSMI_MIN</th>\n",
       "      <th>EVSOH</th>\n",
       "      <th>EVBMA_Latest</th>\n",
       "      <th>EVBMA_Max</th>\n",
       "      <th>EVBMA_Min</th>\n",
       "      <th>EVBMI_Latest</th>\n",
       "      <th>EVBMI_Max</th>\n",
       "      <th>EVBMI_Min</th>\n",
       "      <th>EVBOA_AVG</th>\n",
       "      <th>EVBOA_MAX</th>\n",
       "      <th>EVBOA_MIN</th>\n",
       "      <th>EVBOV_AVG</th>\n",
       "      <th>EVBOV_MAX</th>\n",
       "      <th>EVBOV_MIN</th>\n",
       "      <th>EVIRP</th>\n",
       "      <th>EVIRN</th>\n",
       "      <th>EVSOMA</th>\n",
       "      <th>EVSOMI</th>\n",
       "      <th>EVIGM_Latest</th>\n",
       "      <th>EVIGM_Max</th>\n",
       "      <th>EVIGM_Min</th>\n",
       "      <th>EVCOM_Latest</th>\n",
       "      <th>EVCOM_Max</th>\n",
       "      <th>EVCOM_Min</th>\n",
       "      <th>EVICO_Latest</th>\n",
       "      <th>EVICO_Max</th>\n",
       "      <th>EVICO_Min</th>\n",
       "      <th>EVIRT_Latest</th>\n",
       "      <th>EVIRT_Max</th>\n",
       "      <th>EVIRT_Min</th>\n",
       "      <th>EVIDC</th>\n",
       "      <th>EVMGT</th>\n",
       "      <th>EVMGS</th>\n",
       "      <th>EVMGF</th>\n",
       "      <th>EVMGR</th>\n",
       "      <th>EVIND</th>\n",
       "      <th>EVICM</th>\n",
       "      <th>EVCPW</th>\n",
       "      <th>EVCPF_Latest</th>\n",
       "      <th>EVCPF_Max</th>\n",
       "      <th>EVCPF_Min</th>\n",
       "      <th>EVCI1_Latest</th>\n",
       "      <th>EVCI1_Max</th>\n",
       "      <th>EVCI1_Min</th>\n",
       "      <th>EVCI2_Latest</th>\n",
       "      <th>EVCI2_Max</th>\n",
       "      <th>EVCI2_Min</th>\n",
       "      <th>EVCBD_Latest</th>\n",
       "      <th>EVCBD_Max</th>\n",
       "      <th>EVCBD_Min</th>\n",
       "      <th>EVCRP</th>\n",
       "      <th>EVACV_AVG</th>\n",
       "      <th>EVACV_Max</th>\n",
       "      <th>EVACV_Min</th>\n",
       "      <th>EVCDO_AVG</th>\n",
       "      <th>EVCDO_Max</th>\n",
       "      <th>EVCDO_Min</th>\n",
       "      <th>EVCCO_AVG</th>\n",
       "      <th>EVCCO_Max</th>\n",
       "      <th>EVCCO_Min</th>\n",
       "      <th>EVSDT</th>\n",
       "      <th>EVCHC</th>\n",
       "      <th>EVCHT_AVG</th>\n",
       "      <th>EVCHT_Max</th>\n",
       "      <th>EVCHT_Min</th>\n",
       "      <th>EVCHE</th>\n",
       "      <th>EVDI1</th>\n",
       "      <th>EVDI2</th>\n",
       "      <th>EVDIT</th>\n",
       "      <th>EVOII</th>\n",
       "      <th>EVDOA</th>\n",
       "      <th>EVDOV</th>\n",
       "      <th>EVDSE</th>\n",
       "      <th>EVACS</th>\n",
       "      <th>EVPSS</th>\n",
       "      <th>EVMSC</th>\n",
       "      <th>EVRER</th>\n",
       "      <th>EVDRV</th>\n",
       "      <th>EVMTR</th>\n",
       "      <th>EVODO</th>\n",
       "      <th>EVOAS</th>\n",
       "      <th>EVHTR</th>\n",
       "      <th>EVACE</th>\n",
       "      <th>EVTRE</th>\n",
       "      <th>EVCLC</th>\n",
       "      <th>EVBFN</th>\n",
       "      <th>EVIST</th>\n",
       "      <th>EVHTP_AVG</th>\n",
       "      <th>EVHTP_Max</th>\n",
       "      <th>EVHTP_Min</th>\n",
       "      <th>EVVBT</th>\n",
       "      <th>EVGSM</th>\n",
       "      <th>EVACO_X</th>\n",
       "      <th>EVACO_Y</th>\n",
       "      <th>EVACO_Z</th>\n",
       "      <th>Unnamed: 164</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DEFREG:358272088715191</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.951081</td>\n",
       "      <td>28.390039</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>76.951081</td>\n",
       "      <td>28.390039</td>\n",
       "      <td>87.78603</td>\n",
       "      <td>1036056339</td>\n",
       "      <td>5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>7</td>\n",
       "      <td>358272088715191</td>\n",
       "      <td>Trip not started</td>\n",
       "      <td>P</td>\n",
       "      <td>M1_POCEV.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>246</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>60.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>13.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.88</td>\n",
       "      <td>78.4</td>\n",
       "      <td>78.4</td>\n",
       "      <td>78.1</td>\n",
       "      <td>78.1</td>\n",
       "      <td>100</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-505.0</td>\n",
       "      <td>-357.0</td>\n",
       "      <td>257.75</td>\n",
       "      <td>174.0</td>\n",
       "      <td>16180.0</td>\n",
       "      <td>1790</td>\n",
       "      <td>1790</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>14</td>\n",
       "      <td>-50</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>106</td>\n",
       "      <td>202</td>\n",
       "      <td>15</td>\n",
       "      <td>-50</td>\n",
       "      <td>-16</td>\n",
       "      <td>14</td>\n",
       "      <td>88</td>\n",
       "      <td>212</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>260</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.225</td>\n",
       "      <td>52.9</td>\n",
       "      <td>0</td>\n",
       "      <td>-65532</td>\n",
       "      <td>0</td>\n",
       "      <td>2572</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.7</td>\n",
       "      <td>50.0</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "      <td>-820</td>\n",
       "      <td>654</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DEFREG:358272088715191</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.951081</td>\n",
       "      <td>28.390039</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>76.951081</td>\n",
       "      <td>28.390039</td>\n",
       "      <td>87.78603</td>\n",
       "      <td>1036056339</td>\n",
       "      <td>5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>7</td>\n",
       "      <td>358272088715191</td>\n",
       "      <td>Trip not started</td>\n",
       "      <td>P</td>\n",
       "      <td>M1_POCEV.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>246</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>60.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>13.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.88</td>\n",
       "      <td>78.4</td>\n",
       "      <td>78.4</td>\n",
       "      <td>78.1</td>\n",
       "      <td>78.1</td>\n",
       "      <td>100</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-505.0</td>\n",
       "      <td>-357.0</td>\n",
       "      <td>257.75</td>\n",
       "      <td>174.0</td>\n",
       "      <td>16180.0</td>\n",
       "      <td>1790</td>\n",
       "      <td>1790</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>14</td>\n",
       "      <td>-50</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>106</td>\n",
       "      <td>202</td>\n",
       "      <td>15</td>\n",
       "      <td>-50</td>\n",
       "      <td>-16</td>\n",
       "      <td>14</td>\n",
       "      <td>88</td>\n",
       "      <td>212</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>260</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.225</td>\n",
       "      <td>52.9</td>\n",
       "      <td>0</td>\n",
       "      <td>-65532</td>\n",
       "      <td>0</td>\n",
       "      <td>2572</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.7</td>\n",
       "      <td>50.0</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "      <td>-820</td>\n",
       "      <td>654</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DEFREG:358272088715191</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.951081</td>\n",
       "      <td>28.390039</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200</td>\n",
       "      <td>76.951081</td>\n",
       "      <td>28.390039</td>\n",
       "      <td>87.78603</td>\n",
       "      <td>1036056339</td>\n",
       "      <td>5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>7</td>\n",
       "      <td>358272088715191</td>\n",
       "      <td>Trip not started</td>\n",
       "      <td>P</td>\n",
       "      <td>M1_POCEV.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>246</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>60.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>13.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.88</td>\n",
       "      <td>78.4</td>\n",
       "      <td>78.4</td>\n",
       "      <td>78.1</td>\n",
       "      <td>78.1</td>\n",
       "      <td>100</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-505.0</td>\n",
       "      <td>-357.0</td>\n",
       "      <td>257.75</td>\n",
       "      <td>174.0</td>\n",
       "      <td>16180.0</td>\n",
       "      <td>1790</td>\n",
       "      <td>1790</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>14</td>\n",
       "      <td>-50</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>106</td>\n",
       "      <td>202</td>\n",
       "      <td>15</td>\n",
       "      <td>-50</td>\n",
       "      <td>-16</td>\n",
       "      <td>14</td>\n",
       "      <td>88</td>\n",
       "      <td>212</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>260</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.225</td>\n",
       "      <td>52.9</td>\n",
       "      <td>0</td>\n",
       "      <td>-65532</td>\n",
       "      <td>0</td>\n",
       "      <td>2572</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.7</td>\n",
       "      <td>50.0</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "      <td>-820</td>\n",
       "      <td>654</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DEFREG:358272088715191</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.951081</td>\n",
       "      <td>28.390039</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300</td>\n",
       "      <td>76.951081</td>\n",
       "      <td>28.390039</td>\n",
       "      <td>87.78603</td>\n",
       "      <td>1036056339</td>\n",
       "      <td>5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>7</td>\n",
       "      <td>358272088715191</td>\n",
       "      <td>Trip not started</td>\n",
       "      <td>P</td>\n",
       "      <td>M1_POCEV.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>246</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>60.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>13.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.88</td>\n",
       "      <td>78.4</td>\n",
       "      <td>78.4</td>\n",
       "      <td>78.1</td>\n",
       "      <td>78.1</td>\n",
       "      <td>100</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-505.0</td>\n",
       "      <td>-357.0</td>\n",
       "      <td>257.75</td>\n",
       "      <td>174.0</td>\n",
       "      <td>16180.0</td>\n",
       "      <td>1790</td>\n",
       "      <td>1790</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>14</td>\n",
       "      <td>-50</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>106</td>\n",
       "      <td>202</td>\n",
       "      <td>15</td>\n",
       "      <td>-50</td>\n",
       "      <td>-16</td>\n",
       "      <td>14</td>\n",
       "      <td>88</td>\n",
       "      <td>212</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>260</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.225</td>\n",
       "      <td>52.9</td>\n",
       "      <td>0</td>\n",
       "      <td>-65532</td>\n",
       "      <td>0</td>\n",
       "      <td>2572</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.7</td>\n",
       "      <td>50.0</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "      <td>-820</td>\n",
       "      <td>654</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DEFREG:358272088715191</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.951081</td>\n",
       "      <td>28.390039</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>400</td>\n",
       "      <td>76.951081</td>\n",
       "      <td>28.390039</td>\n",
       "      <td>87.78603</td>\n",
       "      <td>1036056339</td>\n",
       "      <td>5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>7</td>\n",
       "      <td>358272088715191</td>\n",
       "      <td>Trip not started</td>\n",
       "      <td>P</td>\n",
       "      <td>M1_POCEV.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>246</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>60.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>13.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.88</td>\n",
       "      <td>78.4</td>\n",
       "      <td>78.4</td>\n",
       "      <td>78.1</td>\n",
       "      <td>78.1</td>\n",
       "      <td>100</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-505.0</td>\n",
       "      <td>-357.0</td>\n",
       "      <td>257.75</td>\n",
       "      <td>174.0</td>\n",
       "      <td>16180.0</td>\n",
       "      <td>1790</td>\n",
       "      <td>1790</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>14</td>\n",
       "      <td>-50</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>106</td>\n",
       "      <td>202</td>\n",
       "      <td>15</td>\n",
       "      <td>-50</td>\n",
       "      <td>-16</td>\n",
       "      <td>14</td>\n",
       "      <td>88</td>\n",
       "      <td>212</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>260</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.225</td>\n",
       "      <td>52.9</td>\n",
       "      <td>0</td>\n",
       "      <td>-65532</td>\n",
       "      <td>0</td>\n",
       "      <td>2572</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.7</td>\n",
       "      <td>50.0</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "      <td>-820</td>\n",
       "      <td>654</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       mo  tp  dr         ln         lt   hd  sp   tm  \\\n",
       "0  DEFREG:358272088715191   1 NaN  76.951081  28.390039  1.0 NaN    0   \n",
       "1  DEFREG:358272088715191   1 NaN  76.951081  28.390039  1.0 NaN  100   \n",
       "2  DEFREG:358272088715191   1 NaN  76.951081  28.390039  1.0 NaN  200   \n",
       "3  DEFREG:358272088715191   1 NaN  76.951081  28.390039  1.0 NaN  300   \n",
       "4  DEFREG:358272088715191   1 NaN  76.951081  28.390039  1.0 NaN  400   \n",
       "\n",
       "          mn         mt        mh          ml  mr  SpeedLimit  hdop  numsat  \\\n",
       "0  76.951081  28.390039  87.78603  1036056339   5        30.0   1.8       7   \n",
       "1  76.951081  28.390039  87.78603  1036056339   5        30.0   1.8       7   \n",
       "2  76.951081  28.390039  87.78603  1036056339   5        30.0   1.8       7   \n",
       "3  76.951081  28.390039  87.78603  1036056339   5        30.0   1.8       7   \n",
       "4  76.951081  28.390039  87.78603  1036056339   5        30.0   1.8       7   \n",
       "\n",
       "              IMEI           trip_id EVTmg       EVVer  EVCfg  EVIGS  EVIGC  \\\n",
       "0  358272088715191  Trip not started     P  M1_POCEV.0    NaN      1    246   \n",
       "1  358272088715191  Trip not started     P  M1_POCEV.0    NaN      1    246   \n",
       "2  358272088715191  Trip not started     P  M1_POCEV.0    NaN      1    246   \n",
       "3  358272088715191  Trip not started     P  M1_POCEV.0    NaN      1    246   \n",
       "4  358272088715191  Trip not started     P  M1_POCEV.0    NaN      1    246   \n",
       "\n",
       "   EVVSP  EVDRG  EVGPO  EVBRP  EVCFN  EVICR  EVTRQ  EVCST  EVDDC  EVBCA  \\\n",
       "0    0.0     87     10      0      0      1    0.0      1   13.9      0   \n",
       "1    0.0     87     10      0      0      1    0.0      1   13.9      0   \n",
       "2    0.0     87     10      0      0      1    0.0      1   13.9      0   \n",
       "3    0.0     87     10      0      0      1    0.0      1   13.9      0   \n",
       "4    0.0     87     10      0      0      1    0.0      1   13.9      0   \n",
       "\n",
       "   EVBBV  EVDR1  EVDR2  EVRGT  EVACP  EVBAP_Latest  EVBAP_Max  EVBAP_Min  \\\n",
       "0   60.5      0      0      0    0.0             0          0          0   \n",
       "1   60.5      0      0      0    0.0             0          0          0   \n",
       "2   60.5      0      0      0    0.0             0          0          0   \n",
       "3   60.5      0      0      0    0.0             0          0          0   \n",
       "4   60.5      0      0      0    0.0             0          0          0   \n",
       "\n",
       "   EVCCS  EVCM1  EVCTM  EVCCU  EVCSD  EVVCE  EVPSC_Latest  EVPSC_Max  \\\n",
       "0      0    NaN    NaN    NaN    NaN    NaN           NaN        NaN   \n",
       "1      0    NaN    NaN    NaN    NaN    NaN           NaN        NaN   \n",
       "2      0    NaN    NaN    NaN    NaN    NaN           NaN        NaN   \n",
       "3      0    NaN    NaN    NaN    NaN    NaN           NaN        NaN   \n",
       "4      0    NaN    NaN    NaN    NaN    NaN           NaN        NaN   \n",
       "\n",
       "   EVPSC_Min  EVVOU  EVCOU  EVCPV  EVVCD  EVCCD  EVCSC  EVEST  EVCHS  EVR10  \\\n",
       "0        NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "1        NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "2        NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "3        NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "4        NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "\n",
       "   EVRMN  EVHVS  EVV12  EVPWA_MAX  EVPWA_MIN  EVMCV_MAX  EVMCV_MIN  EVSMA_MAX  \\\n",
       "0    NaN     10   13.8          0          0       3.89       3.88       78.4   \n",
       "1    NaN     10   13.8          0          0       3.89       3.88       78.4   \n",
       "2    NaN     10   13.8          0          0       3.89       3.88       78.4   \n",
       "3    NaN     10   13.8          0          0       3.89       3.88       78.4   \n",
       "4    NaN     10   13.8          0          0       3.89       3.88       78.4   \n",
       "\n",
       "   EVSMA_MIN  EVSMI_MAX  EVSMI_MIN  EVSOH  EVBMA_Latest  EVBMA_Max  EVBMA_Min  \\\n",
       "0       78.4       78.1       78.1    100          15.5       15.5       15.5   \n",
       "1       78.4       78.1       78.1    100          15.5       15.5       15.5   \n",
       "2       78.4       78.1       78.1    100          15.5       15.5       15.5   \n",
       "3       78.4       78.1       78.1    100          15.5       15.5       15.5   \n",
       "4       78.4       78.1       78.1    100          15.5       15.5       15.5   \n",
       "\n",
       "   EVBMI_Latest  EVBMI_Max  EVBMI_Min  EVBOA_AVG  EVBOA_MAX  EVBOA_MIN  \\\n",
       "0          15.0       15.0       15.0        0.5     -505.0     -357.0   \n",
       "1          15.0       15.0       15.0        0.5     -505.0     -357.0   \n",
       "2          15.0       15.0       15.0        0.5     -505.0     -357.0   \n",
       "3          15.0       15.0       15.0        0.5     -505.0     -357.0   \n",
       "4          15.0       15.0       15.0        0.5     -505.0     -357.0   \n",
       "\n",
       "   EVBOV_AVG  EVBOV_MAX  EVBOV_MIN  EVIRP  EVIRN  EVSOMA  EVSOMI  \\\n",
       "0     257.75      174.0    16180.0   1790   1790     100     100   \n",
       "1     257.75      174.0    16180.0   1790   1790     100     100   \n",
       "2     257.75      174.0    16180.0   1790   1790     100     100   \n",
       "3     257.75      174.0    16180.0   1790   1790     100     100   \n",
       "4     257.75      174.0    16180.0   1790   1790     100     100   \n",
       "\n",
       "   EVIGM_Latest  EVIGM_Max  EVIGM_Min  EVCOM_Latest  EVCOM_Max  EVCOM_Min  \\\n",
       "0            14        -50          2            14        106        202   \n",
       "1            14        -50          2            14        106        202   \n",
       "2            14        -50          2            14        106        202   \n",
       "3            14        -50          2            14        106        202   \n",
       "4            14        -50          2            14        106        202   \n",
       "\n",
       "   EVICO_Latest  EVICO_Max  EVICO_Min  EVIRT_Latest  EVIRT_Max  EVIRT_Min  \\\n",
       "0            15        -50        -16            14         88        212   \n",
       "1            15        -50        -16            14         88        212   \n",
       "2            15        -50        -16            14         88        212   \n",
       "3            15        -50        -16            14         88        212   \n",
       "4            15        -50        -16            14         88        212   \n",
       "\n",
       "   EVIDC  EVMGT  EVMGS  EVMGF  EVMGR  EVIND  EVICM  EVCPW  EVCPF_Latest  \\\n",
       "0    0.0   -0.1      0    0.0    0.0    260      3    NaN           NaN   \n",
       "1    0.0   -0.1      0    0.0    0.0    260      3    NaN           NaN   \n",
       "2    0.0   -0.1      0    0.0    0.0    260      3    NaN           NaN   \n",
       "3    0.0   -0.1      0    0.0    0.0    260      3    NaN           NaN   \n",
       "4    0.0   -0.1      0    0.0    0.0    260      3    NaN           NaN   \n",
       "\n",
       "   EVCPF_Max  EVCPF_Min  EVCI1_Latest  EVCI1_Max  EVCI1_Min  EVCI2_Latest  \\\n",
       "0        NaN        NaN           NaN        NaN        NaN           NaN   \n",
       "1        NaN        NaN           NaN        NaN        NaN           NaN   \n",
       "2        NaN        NaN           NaN        NaN        NaN           NaN   \n",
       "3        NaN        NaN           NaN        NaN        NaN           NaN   \n",
       "4        NaN        NaN           NaN        NaN        NaN           NaN   \n",
       "\n",
       "   EVCI2_Max  EVCI2_Min  EVCBD_Latest  EVCBD_Max  EVCBD_Min  EVCRP  EVACV_AVG  \\\n",
       "0        NaN        NaN           NaN        NaN        NaN      1        NaN   \n",
       "1        NaN        NaN           NaN        NaN        NaN      1        NaN   \n",
       "2        NaN        NaN           NaN        NaN        NaN      1        NaN   \n",
       "3        NaN        NaN           NaN        NaN        NaN      1        NaN   \n",
       "4        NaN        NaN           NaN        NaN        NaN      1        NaN   \n",
       "\n",
       "   EVACV_Max  EVACV_Min  EVCDO_AVG  EVCDO_Max  EVCDO_Min  EVCCO_AVG  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   EVCCO_Max  EVCCO_Min  EVSDT  EVCHC  EVCHT_AVG  EVCHT_Max  EVCHT_Min  EVCHE  \\\n",
       "0        NaN        NaN    NaN    NaN        NaN        NaN        NaN    NaN   \n",
       "1        NaN        NaN    NaN    NaN        NaN        NaN        NaN    NaN   \n",
       "2        NaN        NaN    NaN    NaN        NaN        NaN        NaN    NaN   \n",
       "3        NaN        NaN    NaN    NaN        NaN        NaN        NaN    NaN   \n",
       "4        NaN        NaN    NaN    NaN        NaN        NaN        NaN    NaN   \n",
       "\n",
       "   EVDI1  EVDI2  EVDIT  EVOII  EVDOA  EVDOV  EVDSE  EVACS   EVPSS  EVMSC  \\\n",
       "0     15     14     14      0     27   13.9      0    0.0  46.225   52.9   \n",
       "1     15     14     14      0     27   13.9      0    0.0  46.225   52.9   \n",
       "2     15     14     14      0     27   13.9      0    0.0  46.225   52.9   \n",
       "3     15     14     14      0     27   13.9      0    0.0  46.225   52.9   \n",
       "4     15     14     14      0     27   13.9      0    0.0  46.225   52.9   \n",
       "\n",
       "   EVRER  EVDRV  EVMTR  EVODO  EVOAS  EVHTR  EVACE  EVTRE  EVCLC  EVBFN  \\\n",
       "0      0 -65532      0   2572   13.0      0   13.7   50.0     55      0   \n",
       "1      0 -65532      0   2572   13.0      0   13.7   50.0     55      0   \n",
       "2      0 -65532      0   2572   13.0      0   13.7   50.0     55      0   \n",
       "3      0 -65532      0   2572   13.0      0   13.7   50.0     55      0   \n",
       "4      0 -65532      0   2572   13.0      0   13.7   50.0     55      0   \n",
       "\n",
       "   EVIST  EVHTP_AVG  EVHTP_Max  EVHTP_Min  EVVBT  EVGSM  EVACO_X  EVACO_Y  \\\n",
       "0      0          0          0          0    137      0     -820      654   \n",
       "1      0          0          0          0    137      0     -820      654   \n",
       "2      0          0          0          0    137      0     -820      654   \n",
       "3      0          0          0          0    137      0     -820      654   \n",
       "4      0          0          0          0    137      0     -820      654   \n",
       "\n",
       "   EVACO_Z  Unnamed: 164  \n",
       "0       23           NaN  \n",
       "1       23           NaN  \n",
       "2       23           NaN  \n",
       "3       23           NaN  \n",
       "4       23           NaN  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = outlier_imputation(df, 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wCYLwweuvv5plhgdUD/HLL786EteLIiSwBYb3bAMAfGOfYzF8h0Nx301bcUjyLQRZXYggiHiyzz5Tsc8+UyN7vShCTmci06cKbF5p2t8Qx+2AIAiCKBEksAWGaQJbMQQ2lQMkCIKIKySwBUbq7wAA8Mo69aeR+Yc0bIIgiLhBAltgEv0WJnGCIAgidpDTmUCsWNuMgc+fhzpFFdRH9m0AGMAr6rQjNA2b9rAJIvRQPWxnLF58O0466dSCx2Teo6Io2HffA3DGGT9DRUXhaBrzufVKYJs3b8Kf/3wVLrnkSt/tDxLSsAXi/Q/fxX49L2JYshlVqU70JAZh7Xbz8sRck8AmiLCjF//Q/5144imYMWMWli17Nus4vR72LbfchTvv/Ad++MOfYNq0GcbnRo4cBUDNJX7XXfdizz33wl133QYARj3sAw44GPfe+zDuvfch9Pb24vrr/2LZLr0eNoCsetgAsuph77//QTjxxFMAqPWw160rXF3LK3fffUfB9833eM89D6G7uws335y/OpiTcw8bNtyVsC6UKz1ISMMWCUXN7lM383eo3Fm8tHgEEUWqVj+A6g/vDeRcjDFwztEz/nj07naM689HvR52b28v/vjH/8Xq1f+BLMs466xF+Na39s6qXw0Av/jFOTj++P/B66+/aqRw3XHHnfC7312S016re1yw4Gj84Ac/xvPPP+v63F99tQG/+MU5uPvufyKVSuHGG6/DypUr0N/fh3nzFmDu3O/i7bffwh133IKhQ4dhzZqPccsti3Hhhf8PGzduhKKkcMopP8T06e5TyxaCBLZQaJozszJ8kEGEIKJCHOthP/TQ/QCAxYvvw2efrcO5556Be+55yPJeTj/9LDz00D/zliHVsbrHESNG4Isv1vs6NwA8/vgjGDBgAG69dTH6+vpw+uk/wLe//R0A6gJm8eL7MGrUN/DCC8swbNhwXHXVtQCAjo6Oguf1AglskeB6/lwK3yKIUtG72zGetOF8UD3sNPnqYb/77iocc4z6+TFjdkBT00isX/+5o/NZUegeg6ip/eabr2HNmjV44YXnAajlSb/4Yj0SiQTGj5+AUaO+AQDYaaex+NvfrsX11/8F++13ACZN2tP3tc2QyiYSvLCGzZnpOIIgIke062Hnn7sya14DQG9vn6NrANb3uHFjC77xje18nRtQFwTnnnu+4TNw//2PGhq2XjkMALbffgxuu+1u7LzzWNx443W4445bXF3HCSSwRcIQxFarQorDJog4ENV62JMm7YmlS58CAHz++WdoaWnG9tuPQVPTKKxZ8zEURUFLSzM+/PAD4zOynDBqYOfD6h4PO+wIVFdX+zo3AHz721OxZMkDxnGff/4Zuru7c47bvHkTqqqqMWvW4fje907Exx+vtu0Pt5DAFgrNlGa5h00QRFSIYz3sefMWQFEUnHTScfjd7y7Ar399ESorK7HHHpMwcuQonHTS8fjrX/+EXXdN760fddQ8nHzy8bj44t/kPad+j//6l3qP3/vefFRWVuLHPz4DALLO/be/Xevq3ABw5JFzscMOO+HUU7+PE088FldddVler/BPP12D0047GaeccgIWL77dto+9QPWwy0xmDeI7lyzB+V+eibY5d6Fvh+k5x95zy+9xdt/N2LxwFXjtsFI3VVjiVMe5mMSpH6keNtXDLhdUDzsyqGsnTho2QRBFgOphhxsS2ALBdC9xK89GyiVOEERAhK0+dVtbK372s5/mvH7ttddj0KCGMrSo9JDAFghu5yVOqUkJggiIsNWnHjSowTZmOuqQ7VUkKA6bIEqGQO47REzwO+ZIYAsEs810ZjqOIAhPJBKV6OxsJ6FNlAzOOTo725FI5C+64gQyiQsETR4EURoGDx6OrVs3oaOjNdDz6rnECe9EuQ8TiUoMHjzc++cDbAvhF5s9bEqcQhDBIMsJDBs2MvDzxik0rlhQH1pDJnGBYCjsJc4ZOZ0RBEHEFRLYIqEJYk5fC0EQBGGCJINAcLs4bDKJEwRBxBYS2ALh1EucIAiCiB/kdCYSdnvTxh528ZtCEGHi440d6HnkpxidStdWrqobitrj7gYqasvYMoIIDlLlRIIXrtbF8/xGEATw7pdtmN73POrQhc7EEKRSKYxpex3yNuvKUQQRNkhgC4VdPWyCIPLBuVqruHKP47HdDx/CkwOP098oY6sIIlhIYIuEbRy2lH0cQRAAACWlCmwmm3f56FkhogMJbJFw7CVOEEQmXBfYkqy+QJXtiAhCAlsonMZh0yREEJlwRV3sMknXsGlxS0QPEtgCYV8PW/+FBDZBZMIVVcOWzCZx2j4iIgQJbJEw9rBJOyAIN3AlBQBgstkkThDRgQS2UNh5iVMucYLIh65hp03i+hv0rBDRwbHAvuCCCzB16lQcccQRxmtXXHEFZs+ejSOPPBJnnHEG2tvbjfduuukmzJgxA7NmzcJLL70UbKujCi8ssDntyxFEfjSnMzBNw6ZnhYggjgX2/Pnzceutt2a9tt9+++Hxxx/HY489hh122AE33XQTAGDNmjV44okn8MQTT+DWW2/FxRdfjFQqFWzLI0nhxClpSGsgiEz0OGyuadh6ZTtGzwoRIRwL7ClTpmDQoEFZr+2///5IJNQHZPLkyWhubgYALFu2DHPmzEFlZSVGjx6NMWPG4N133w2w2VHFZg+bymsSRF64rhDkaNj0rBDRIbBc4g8++CAOO+wwAEBLSwsmTZpkvDdixAi0tLTYnkOWGRoa4pX3V5Yl454T2hwzsL4WyNMPsqQeUF9fk/f9uJLZh4R3wtyPFdpMVltXg5qGWiQSqi5SV1dV0mclzH0oCtSH1gQisG+44QbIsoyjjjoKAMDzaIDMgddmKsXR2toVRJNCQ0NDrXHPqZRqEt+2rQcpObcftLexrb0LKSle/VSIzD4kvBPmfuzr7QUAdHan0NfahWRKnYM6tvUgWVO6ewpzH4pCHPtw+PCBjo7zLbAffvhhvPDCC7jzzjsNodzU1GSYxwFV425sbPR7qeijxWFzqz1s8qMhiPzoJnGJTOJEdPEV1rV8+XLccsstuOGGG1BTU2O8Pm3aNDzxxBPo6+vD+vXrsW7dOuyxxx6+Gxt1GBX/IAhvKLqXuKaDkL8HEUEca9iLFi3CG2+8ga1bt+LAAw/EWWedhZtvvhl9fX1YuHAhAGDSpEn4/e9/j1122QWHHXYYDj/8cMiyjAsvvBCyntCAsMa2+AdNQgSRD851pzPzs0PPChEdHAvsa665Jue1BQsWWB5/+umn4/TTT/fWqthCGjZBeELJDusiiChCmc6EonAcNqcKRASRF6Zr2ObiH2SNIiIECWyBYIZJ3PKIUjWFIMKFsYctZf+kxS0RIUhgC4XdHrZ+GE1CBJGJXvyDTOJElCGBLRBGeU274h+kNRBEFunStDSlEdGFRreAWMZhEwSRH90kLlFYFxFdSDIIhGMNmyYhgsiC6V7iWhw2J2sUEUFIYAuFzR62g/SuBBFL9MWuZHp2aHFLRAgS2AKR9hK3E8w0CRFEFtwi0xlBRAgS2EJBTmcE4YV0HLaaUZGB6mET0YMEtlA41bAJgsiE6WFdej1sSjJERBAS2ALBbHOJa9C+HEFkw01e4sbr9KwQ0YGyDAgEszOJk+ZNEACAlz9pwR7PHIVR2AQAOAMp9bGhxClEhKHRLRKaMkBx2ARRmPUbN2Me+xKfDtgTG2p2AwCMGDkGg2sbAQAclJqUiB4ksAXCVsOmOGyCAABwTRAP3eNI1H/rtNwDKHEKEUFIlROKwk5nnEziBAEgY0lLzwQRI0hgi4TDOGwKVSHiDteeFWaxfURinIgiJLAFIi2Irb4WmoYIQsWm2AeZxIkIQgJbIIw9bMp0RhAF4YquYVu8T0mGiAhCAlsk9LmFtAaCKIhtGl9KnEJEEBLYAuG8HjZBxBuuWaMYPRNEjCCBLRDMcWpS0hqImGObFZCsUUT0IIEtFBxKIY2BTOIEASBjyWqxuGVkEiciCAlsoeAZzjIEQViiFfugJENEnCCBLRASFDjbp6ZJiIg7VNmOiB8ksEWC2+URp8mJIABk2sTzv82oHjYRPUhgCwSD4swkTmY+IubwdAykxRFkEieiBwlsgWB2e9iMKhARBABDEFtZpCjci4giJLBFgjvdwyaIuOM0KyBBRAcS2ALBQBW5CMIRio1J3HiZrFFEdCCBLRC2e9gkzAkCQLpal/XjIukHlqI5BFESSGCLBOdwZBKnSYiIPYU1bE6JU4gIQgJbIBh4QZM4VSAiCB07L3GCiB6Jcjcgzry6ei1GvPIrVKc6AQCHSp+h4AREJnGCULHJJc7MxxFEBCANu4x89J+3sE/3SxiS3IiqVCe2JYahZcw8+w/SJETEHiqvScQP0rAFYOARV6F69AG2x1FsKUFo2JaipZwFRPQgDbuceNSUKd0iEXs47WET8YMEdpggMx9BAEinJrV00jQeFXpWiOhAAruMMPJ0JQhv2ApiWtwS0YMEdhghrYGIOzZe4oY1ip4VIkKQwBYBh+FanL4ugtCgethE/CAJUE48r/5JayBijp2XOAlyIoKQwA4TZOYjCBUbL3E9BJLRs0JECBLYZcTtVEI6A0GoGM8OJU4hYoRjgX3BBRdg6tSpOOKII4zXWltbsXDhQsycORMLFy5EW1sbALWSziWXXIIZM2bgyCOPxAcffBB8ywmCiC2M4rCJGOJYYM+fPx+33npr1ms333wzpk6diqVLl2Lq1Km4+eabAQDLly/HunXrsHTpUvzhD3/ARRddFGijIwN36ThD+3IEoaHtYVt5iVNYFxFBHAvsKVOmYNCgQVmvLVu2DHPnzgUAzJ07F88991zW64wxTJ48Ge3t7di4cWOAzY45tC9HxBxu9wyQvwcRQXztYX/99ddobGwEADQ2NmLLli0AgJaWFjQ1NRnHNTU1oaWlxc+lIo7TsK5craG3rw89XduMfzzZW4T2EYRo2FmnSMMmokdRin/kW/0yB+ZcWWZoaKgtRpOEpEJW+6SurhrcwX1XVCS046vAG2rx8prNGHPPQdhJ+so4ppdVQzp7FVDXWJxGC4gsS7EaN8UiTP1YkVB1jQF11ajN0+bKShkAUFtbhZoS3lOY+lBUqA+t8SWwhw4dio0bN6KxsREbN27EkCFDAKgadXNzs3Fcc3OzoYkXIpXiaG3t8tOkUJFMpQAAHR096Hdw3/3J7OM/2dCGA1kz1tZ9C+sHT0X3l+9jlvIitrSsRypZV9S2i0RDQ22sxk2xCFM/JvuTAICOzr68z05fv7rH3dXZg94S3lOY+lBU4tiHw4cPdHScL5P4tGnTsGTJEgDAkiVLMH369KzXOedYtWoVBg4c6EhgxxbHzmTZx3Htlbqd9sX4oy7A+3X7Bd0yghASbpealEziRARxrGEvWrQIb7zxBrZu3YoDDzwQZ511Fk477TScc845eOCBBzBy5Ehce+21AICDDjoIL774ImbMmIGamhpcdtllRbuBOMFMjjScc0iM5wp8crQhIo9dWFf5BfanG5rRu/ppSFy1BiQkhp0mHQJpyE5laxMRbhwL7GuuuSbv63fddVfOa4wx/O53v/PeqtjhNlxLE9gKxaIS8YTZpCZ14jNTbF5/8lb8rPfGrNe+2vQcpGPvKVOLiLBTFKczwiGuNWGzSdzkKSvAJEUQJcFpDoMyGpskpQ8A8NHhj+Cr3irUPXs2Rve1l69BROghgS0A3K2GrE9W2k/GJNqpI2KF7XgXIjWpeu2ho8Yi2VeNr/gAjEnGy5mKCBbKJV5GuMvJhJu1CcWmYhFBRBU7DZuZjisz1RUSelAFOdVT7qYQIYYEdqjQKhDpe9g5ySNIcBPxgNmmJhVpamOoTsjo5pVIKCSwCe+INKrjh9tc4ubPaQKbSZLF+wQRdeyenfI9C5mlPStkhh5WhQRp2IQPSGCHCGZ2OuMp4x0gj8mcICIK17zELf0/mED1sBkDYwz9rJo0bMIX5HQmBB4FrW4VJEFNxA0b6xQTIA7bfO2kVIVK3gtwpYApnyhEb1LBZx+vRKJ3i/HadtvtiMrhY8vYqtJBAruMMLeTiTk/irGHLZkOE0CrIIgiwuwSpwi4hk3K1UAKQLIXqKgpd3NCyUOvvod0DH5mAAAgAElEQVQz35mrJozS6GNVaPvxakCuKGPLSgMt80TAsYasf116WFdK+zg5nRExw7H/Rxn3sE2LiqRcrf5F+9ieSfZsg8Q4Ph37A7w+9XY8hOma1SJl/+EIQBp2OfG6v2bEYWt/50xapGETccEmNakIe9gaKU1g89sOhKItvnt3nYuaGReXs1nhQvs+h4+ZiPrdZmLFG8tUq0VMIIEdIsxOZekypmQoIWKGrlFZxmGbrFFlRHeM22nKUXhp1edI8D6kFGDHjjdRs+HfZW5duEgXfcl5p9RNKQsksEOJXvzDZBIn5zMidhT2Ei8rJhmy78RvAhOvBwBs6erDe7cehynKFt13lHCFAN9vGSDVLESYh6jxN1XrIuJGCHKJF6JCktAPGZLSX+6mhAyzs2G8BDcJbCFwOOjylNdUX47XoCUIu/KaLOe4cmC9qKiQGfqQIIHtGos+FXRhFjQksMsJ92oM00enHogta3+T4CZiAs8f0mhgLG7FNDhXyBL6eAUkTgLbFTx7oea6cFLIIYEdKrK/Lr0edu4CPibLTSLG6Pn0rTRskaa23DbKEkOSJSApyTK0J8xYzW3xmPNEGtUxxGMucT2HuNdc5AQRchg30vwVPM5tRbwgsUtglGIJyLyvRK2JCDGf80hgh4mcbRvd3Kd9jTEdxEQMsXGs5CZ/j7Ji8VimWAVkThq2O8jpjCg7TgedldMZVesiYoqdhl3WR8FOw66AzPvL3chwwS2cDWPShySwQ4me6cwqiQBBRB0bL3FJ/IdCkSoggccmrWYQ6Fscca1MSAJbBJwOvpxQBn0fj75GImbYeYkbx5XRS9xQ+vI/3ymmFatIkae4Z2Imt2mmLyduzTjmfTkr81BMPCaJOGNTrctcKEdAFEkV2EwhxzPHWDidxaVCIQnsEMNj7oBBxJnCXuJ6MiEuwt6mRRu5pGvYJLAdY6mkxAMS2GXF3eBjFo4WnEziRMxgNhO3AGIatk5nuoZNJnEXcNNf8RLcNNOHEGOysopFFUGrIIii4jSXuLjPApcq1V/IJO6cmJcUpmpdIuDZ6cykZZCmTcQFm/lZt0aV0yTObCxoukm8feNn6OthkBnDoKEjALmqRC0MI7oTYTy3AUlglxPXk4k+OLPjsONaG5aIMzbbQQLVw7aCV9QCAHZc+n3jtZaGvSB9/5FyNUl8Yr6HTQJbCLylJk3/JM2aiBdGalKrOGyh4nTzt2XajGPw/CqGRKoH/QrHsDX3YfuejaDcZ9akI+XiuQ1IAruseAzrMj7uLJ8yQUQPmz1sZjquDNiFGo0eNgijDz0VANDRm8R7n7yA7fn6UjQttJidDeOWQIVUszDCzRp2vAYtQdglJUkfJ4Dm5VCoxM3j2RfkdEaUGn216PxBtTADkbMZETvsFqvZTmftPf1Y8ei12KHrHeOIurp6NB5xCXj14CK30R7G1KPjkgDEO4W3QqIOCewwwbKdzmDex4uZeYiIMzZpeU2vv//VNuy78e9oYJ3YyhqQUPowsvNrtDXPR98O0x1d8cO1ayF9/rLxd21VBbafPBu8usHTHWQ1F4w0bCfkrGfi1WcksEXAraA1NHOLRPgimAEJooiwnF/M72en8U0pHDIUbNvhMFTP+QvufPRxnL/+J45zjSdTCtY/djG+Lz+X9frGvrVgB16Q/0NOzfbQNWyGuJh2vWJZ3zwmcx7ZUsuK17Au/ePxLuZOxBgbL/GMAwEACgckKACT1U9pP+GwHnVS4ahGL9oSw/DuYU/i7kn3opUPgNK11UPjc9FFNYuJ4PFOvOc8EtihJL9JnExqRFxgdotVbWbT97AVzjWBrb0haT8VN6UtOSBVYOROe2DgN76JDtSA93c5aKyb55IEdiHc+/1ECxLY5cRz4hT98/rL8fSYJOKMM6czHYWrJnFds2aS9tOhSZxrZ9QTtdRUSOjmVUB/p4M2OoVM4vZYLdTi0W8ksEMEMzudGR6Tkn5AiVtEEOXFStNipkxn+h42NEENSXPf4c40bM6zPbhrKmR0oQro7/bU7tz2qk5n9AQXhudkOouXCIvX3YqKa6cz7QftYRMxxRCediGNuglVF7iSeQ/bocAGB9P+B4CahIxuVEFKWpvE7SqKZR2rN5X2sG2I95xHAjtMmIu2W00I9NATUcd2sZodh50yTOL6HrYmsBWHJnEOSODG9aorJHTxKkjJoDRs8hJ3RzwFNoV1CYHXXOLZqUnJoEY4oWVbL9bedTKOZOmY4j5UgJ38GFC3exlb5gabXOIWe9i6Rs40pzPm2iSuadiaSVxOtcL2DA60QVVU0/NrS05q0vI1pRyQhl1W3OYSt/p4zEYt4YtNHb3Yia9Hc2I7LG9aiMeqjkAl+oHWz8vdNMcwS4dLDWMLW/MSV9SwLmbew3boJc4N/TctsLt5FRIpaw3bVdYypi8HSMMuRNz7hwR2qDBiVdQfyF/8I+6DmiiMqi0CfMhYjP/uH/Dx6O+Vu0kecGbKTsdhc9WkrQlsSdvD5g7jsNVHLm0Sr5AZulk16no3gv1td/Xf9RPR/cadLu4hTXoxQM9uQay2QmKyDUgm8XLiubar+rmcmMSYOmIQQRHGSc9qzGd7iSucI8Ey9rBl9SdPOdWwsxfCjDFUfetEvLauCgBHV18K+3UtQ+dnbwK7Hpt1bUd3oecSD+NXUEJyuydecx4J7DBhFXuY4ylLTz1hjSF8jPEUxknPxkvc9KykdOcyw0tcnfq408QpHDn7zNP2PwTY/xAAwItrNmPb0werWrwHmPE/PbsFsXQ2jEe/BSKw77zzTtx///1gjGHXXXfF5Zdfjo0bN2LRokVoa2vDN7/5TVx55ZWorKwM4nLRw6FmrDvSGJo11cMmgiREZkVmM/b1Z8UIfdScyxiT1QWLrJvE3YV1uV3cKJ4dSol8sJhX6/K9h93S0oLFixfjwQcfxOOPP45UKoUnnngCV199NU455RQsXboU9fX1eOCBB4Job8Tw+XB6NqkTcUYVYu6Fj0ikzdPO6mFzTcPWBbWhYbsyiaNgGBnPuJ5b0olTSGA7ItTWIe8E4nSWSqXQ09ODZDKJnp4eDB8+HK+99hpmzZoFAJg3bx6WLVsWxKUiisNBZxym7WFbmQVDpCkR5YeF0UKjK85WYV1SdlZAXTDre9iGhq04dzrzomG7OZ4b/xGWxDz3hG+T+IgRI3DqqafikEMOQVVVFfbbbz9MmDAB9fX1SCTU0zc1NaGlpcX2XLLM0NBQ67dJoUHze8HA+mrAwX1XVVYAAGpqKlDdUIuENinV1VWDN9QioU1C+t9xQZalWI0bvwxs7wUAyAkZDQ21qKzSPKclCNuPHze3oeXRCzGgfwsA4MDkaoBp7a3MbXNtrbr9VlWVQE1DLaqqtBzgtdVINNSitrYaAFBZKaHawT33y7JasVrKP9bq6jpUDZkBg7T39QW10z7ljIExLux3UCoKPc+yNmnWDawBb6g14ukHDaoBBkS/33wL7La2NixbtgzLli3DwIED8bOf/QzLly/POc7JKj6V4mhtdVD9JiKkFPWB3tbeg1TC/r57+1RtoLu7Dz2tXehPqlpDR2cv+lu70J9Sz9fR0YP+GPVjQ0NtrMaNX7Zt68EgAKmkgtbWLvT1qeZiRRH3+Vv67xU4d9PdaMcAdKMKYMCG+j1R0cmBrtw2d3f3Q+EMvT396G3tQneXukjp6VWQbO1CT696zz1dPehxcM9tHb1gUMt05uujzk71/Jyn+1DfeHDep6qruKjfQako9DwnNUvJtm09SLZ2GT4KbW3dziqnCcrw4QMdHedbYL/yyivYbrvtMGTIEADAzJkzsXLlSrS3tyOZTCKRSKC5uRmNjY1+LxVBXObFZdlx2IYDBnmJE14IUSwr18f0tD9AGq+GTTnazzM5nUmaFUqSJSicuUicorfCyslNM8/zzNfc9SfP+J+wIOZe4r73sEeNGoV33nkH3d3d4Jzj1VdfxdixY7HPPvvgmWeeAQA8/PDDmDZtmu/GEibM+zlh3IskSo658lQocLmY0AOkdEGvpFTrlJ7pTGZACpJjL3HFlEu8OJDTmR1mZ8O4pXP1rWFPmjQJs2bNwrx585BIJDB+/Hgcd9xxOPjgg3Huuefiz3/+M8aPH48FCxYE0d6I4lTDNjnSGD/jNWiJoGBZP6KkpeTk5ubZTmeyxFSBraQcPT2cFw7rYoZybc7A5sbpjOKwbYl5hcJA4rDPPvtsnH322VmvjR49mkK5bGBeTZD6x4zBS17ihHPCmbPaXc4Bo/qVkbNAu18jNakqsJ2axAH3fea+jxnJa6/EZM6jXOIhwjxVpcO6WN73CaIgxoCJ8jSgPSN6+BbTTeJpDdvpWQrFYavGbJZeGBifcxfWFb6FVInhlDiFKBtunc6yTeJxWVUSwWLpOhWK8eQ8K2BmIhM9cQpneggbg+JCw9b3/V1tP3nqzjB8B+Uk/5wZl4UOCewwE/PVJuGXEI0bT9blzD3s7IgK3ekMQaUmNV/PC4yczpwTorEbICSwQwQzymvqgtq0hx1TRwzCHTle4mbLTQQwF9MwTN/asyIxVcN2bBI3nJPdPGPuvMrJedQL8eozEtjlxLPPmckkToKa8EKYxo0x1p0drnttm4t/GE5nmpe4Uw1bvXQBL3H9J3dap7vQWQhLKA6bKDdOV9bccpCGJwEGIQZZtZ2jLCj0Z0HTpLnJ6cztHrZboera6YyeXRviXfCIBHZZ8apimzVsSTtbPAcxERChEBZunM7Sx5pL0coSkOIuBLZ25dxFc/q0nLO09QteHKEkxEVT9IxJw7b6PqIKCewQo5vfcgU1PfSENWm5HJ7JzhwuZYcRh22EdekC2xSH7dTpzCZxSiCw+Hg7eyfeVkUS2OXE5R60XjIwPTZpD5sIgAhmOgOQVa7SSEGqm8T1sC7HXuJ2cdjZ11Nfc5tLnJ5jO/Qtg3RfxavPSGCHEd28Zxb4JLgJBxghSqEaL173LrVCOXm8xNU9bGdOYpwDElM8XN85nHKJOyDeSkogqUkJvzgdfFZVucgkTvghepNf2iSuwqEnTtEFthqHndj4DtYs/iEAoK52AEYe/hvw2uE557PL22/smWeaZrlbrZnFxrTrGcv+iUe/kYZdRlznJrbMdBZP8xDhDUuPZ6GFhcvtIyNVqKZBK9lhXaMH1+D9yj2RUhTsuO0tjG1/DRNbHkTlF//Of3W9z4qs2ZGG7ZKYadqkYYuA50FnUfyDIGKOue4VM+1hD6mtxME/vt54f/Ezy3HemhMs97TTeb7tMp2ZBa6LZ5vpVyJsiZmg1qGZvqy4dUrRfhqakEUFI6E1JaLc5DhQGQu+MIwblxO1uVqXxeKWSeYsgubz6H3m5uLukqiQ05kTrLzES96QskACO0QYCS60yYeZTOIspqtOIgZ4COvKnNTTGrbFlKeZyplFXLZdLvG0l7h3ycGjncImGExzXkzktAGZxMuJ73jYeHtMEh4x72Gz7IVgFGCMQQHDoI/vRcfHj+IHvF81W0v5pzxmCPL8WrGXPexCiVYKfMLF8XEk3qlJSWCHCT2jmZHpTP8Rz8FLxAl3i9O9Rw/C8yN/jKauj4zXBjcMwbBh38z/AU3DtgrzSi9vCmQ6C0DgSvTs2hDv1KQksIXA4+Dj5j3seA5iwh1m824UR83g2koc8N1Fjo+XNGc0zpP5D/CYS9wdUfwmgsa8cIvXrm687lY4/IV1sZivNolgCccoKlIrZc16peR/JtU4bG7Ecee2Kk8cttdMZxHamggas9+OQUz6jAR2mMhJIWnh+RqTwUv4JEy+D0Ue00zXsJX8GrZdalIrXFXrimBd8uAxa9ghGsMBQAK7rFiEZTnF7DEZpgmYKBs5ht0IOp25xhDYNk5nVh/Pk4/dfRIU+h6IwtAedqhQH+i2lQ9iw39W4sDutdrLJKgJL4Rp3BR3+0cvrOM5cUqgkMC2xLJr4tFnJLCFwNkksPPIoVjFxmNUbwvG9m4EADTXjoNcM8R0tngMXsIbnAOMZWqCwRvaXnvvA9R/cAdkTQDKEjDm2/NRseMBgV8rCBhTp0JuFYfNuerBXWBxnJtL3KWAp6guB5hyuodpzRkAJLDLiNuxNmXMMOCnz2a9Jvs6IxFrTMKHW2X58sDa1+7H2X33opNXQwFDLXqw6fUvAZ8Cu1jbPkzWvcQLmcSBkniJk0ncGpcliaMGCWwRiOfYI8pEKcQB0wRf9w/fwifbKlBx75HYzmHt6bwU2+lMszLwlI1J3KoetkUctiunM+NYEthWWEfGxKPPyOmsnAQ8CZlTlxKEE3StlRd93Ag8Lg0N29oknsddryDuK3eTwLaHvMQJgogTNh7PgV0EUCdW5l8E8SK3V7Yr/gGbTGd6j/pa9NCC2xbf6ZzDDQnsskKJT4gyUsR9QJYhdFRDcQBSu4gwxpDirIDTmftc4ukSX04bkfE5wgJKnEJEBZL7hAPSqUlz3wkeFkye7SI7G8mMIQXJUsO2C+vSFyX+LAG6hu3jFJEn+/uJW0lSEtgiEFOPR6LMFHXc5RGwAmtB6qJCstawddHgusvI6awoxLRaFwnsMsKKNoHFY/AS3uAmS62eNKRYTmfM2P0N4vxF0rAlTcO2y3RmlUtctyJkbQVQprOgYebfYqbskMAWgqAGHX2dhBuKuIedsddo7GH7ochCTNJM4lZe4oC3TGdxM9kWHWNrpLzNKBc0w5cVWkkTpSe9H6uje0gHOB4za7UzgHOx03hJhoZsrWEDsMl0Zu5Xl/drJO8St5/KDzmdEVEjJoOX8EmJzImGqPY1LovrdKZr2Ci4h13cXOK0h20Py3E+jJeqTQJbAIIym1G1LsIJdpWnArqK+iNrTIoriCSpsJe4wlEwl7haq968h53+3xm0h21PvENhKTUpQcQWTaTYZDrjnOOxx/6BvTbeb7wmyzJGzvgF+Hb72F+FsQD3coukYUMLy7LQsNPJZoqoYVM9bMeYx1NcthFIwxaBwDXjeAxewivuxGd/iqN+7ROY0LsKNck2VPa1YkLnG0h9/KTlZ3KdzgBf47LIQ9rQsGEVh13YJJ52rPOeS5w0bHtyBHPMFG0S2GUk6FVhzMYu4RtNszb+ttCwoa4puyqHYshP/oU3D74PPai0EaJpb1495IkJLIckBihgBfawtd5ys7j2LHgF7qhyE/NqXSSwCSJm+N3DZrpwc30OP4JI13yL53Sm2MVhswIatp551Zd2TNOxc0hgE6UmHStSpPMSRAEcaimcZ5uD1UIXrHD9bFORBt+pSYuMzBhSvHBqUgCuNDvmMpc4N7awxe2n8kNe4kRUiKmZiHCHeT85XZa1gADOIB2zXEiwpCfWQIZlkU2hhtXAKnGKjVXCag+bCJp4e4mTwBYBErSE8GSbg1XzrzMBr2vkImuOsqSZxK3qYetOZy6fVU9OZyT0LTH8IPTIBv37EHhsBQkJ7LIS9CDTv854DF7CG5nGbfWHOm6s5jz9eH1yNPZ7C0ySLK8mJO64NBKnFMh0VtDpjKl3zHPisN0QL+HjDWeLxKgSiMBub2/H2WefjdmzZ+Owww7DypUr0draioULF2LmzJlYuHAh2tragrhURCENmwgPaQcrJ5NnurymH0c3P2LQCaqXeAGnMxQu/mGNi/ZSHLYLqFqXZy699FIccMABePrpp/HII49g5513xs0334ypU6di6dKlmDp1Km6++eYgLhUxqFoXUQY0JzLm2OksO2kI0zXsgnu62SJW9BFpWA2s4rBtEqeYC5ykHfWcw0nDdoC5b+Kl7PgW2B0dHXjzzTdxzDHHAAAqKytRX1+PZcuWYe7cuQCAuXPn4rnnnvN7KcKOeI1dIiCYy31AXQAX9BI3efP63cPOzSEdLHZx2GlxWvyHLC5Zu7xQ7HEgOr5Tk65fvx5DhgzBBRdcgNWrV2PChAn49a9/ja+//hqNjY0AgMbGRmzZssV3Y6OG2YGCIEqLQw0b2dqi6iVeeA876yqBpiYtDk5yiTMoBXKJ68lhMn3w3aYyJZO4PfGu1uVbYCeTSfznP//Bb3/7W0yaNAmXXHKJZ/O3LDM0NNT6bVJo0LfDBtXXALX+77uiQv06B9RWojZG/SjLUqzGjV9qaqvAwFFRKaO+oRbVNZUA1MQg+fpR7kmCAWCS2s91A6rBAVQkGAZZ9Ls+nTY0DEC33I8WAJIE1Hv8nioqZQDAgAHVRRnbg7qTUCAhISFvH9TWVoIBqKxM5L2HgZ396i9M/byi8Ky/ncBk9R7rB1YD9fEdz4WeZ90aNKihFqiqhaz12cD6aiAGc4Bvgd3U1ISmpiZMmjQJADB79mzcfPPNGDp0KDZu3IjGxkZs3LgRQ4YMsT1XKsXR2trlt0mhQV/Mt7V3g/f5v+/+ftWc19nZi74Y9WNDQ22sxo1furp6AQD9/QpaW7vQ09MHAFA4R3uefuzoTarvK+rz2dXVCwUSlP6kZb/r3tKtbd3Y1tEPnvF5L/T3qQKxWGO7s6MHVWBIJfvztrGzsxcM3OgzMx0dPRgIAFy9RyVD03Z6z7qQb2/vgqLEdzwXfJ61SbOtrQe8MoFUSv17W3sPUonw9tnw4QMdHed7D3v48OFoamrCf//7XwDAq6++ip133hnTpk3DkiVLAABLlizB9OnT/V4qgsQ7RIEoD0aIkoG92TYzBtmdl3jaXCyy2TJdDzuJ3p4u9PZ0IdXfa7xveIk73UZwl+RMxfAlcPvBGMH1H/H0Eg+kvOZvf/tb/PznP0d/fz9Gjx6Nyy+/HIqi4JxzzsEDDzyAkSNH4tprrw3iUhGFqnUR5cDbPqDuJS4XisPOSLubmdDU88gs8pCulCV08QSaWlcAt+0KAEhBwrY5d6J/h2m2cdjqXYq9KIkG8U5NGojAHj9+PB566KGc1++6664gTk84hZzXCAektUWHx5vDujLfsCNrTIorzLZrqMbLe/0CL256FQCwubUV3+28F6ztMwAeNGyXx+ufMf9GZJM/IU98CERgEwQRQnRhamQ6yy8ouJElW890BiicQXYYh61nAfOjffIczSpYGGM4YL/pANStu3v+/QGw6l7wlLp3bsRVW2Y6CyKXuLZDSVp6AdLOfNkvx6PPKDVpWSnSJBSTwUt4w0tSj0zSucEdxGEjN6lIGGCyqstwJZl+DaBc4uUmJ29KuMaVX0hgR4mYDV7CJ7oTmU2GLfXlbKczN/Ww06lJw4MusBVDwwYKxVUbvaH3IfewREnvNbj9ZIwgkzhRdqI7+J58+d/Y6/3fowJq6BADUDXhKNQecG55GxZz/Iy4tNd3YS9xJWPPOzOdiDf0M5TmWZF0DTuVNK7uRcP2lDiFLGSWsByrZLysEqRhlxEW8IMpog7z9ZrXsXvqAyhyFTrlQRiabEbtf58sd7MIAO4dqFQk5qRcZh5tNESCSJayTeJ6tjdmUfzDWMRk5TjzmEs8JsLHH+LNdaWABDZRXLRJuvHYG1F5/H1YpYwFTUjlJW3e1TBmgcLfS1ZxCwcato6x5+2DUueQlmUJ/VxOa9g2xT8CIWa1nb1g7SUejz4jgV1W4p3InigzTsedKQmIWtmq8B52Vsw1y9Q7w4Gs5RbnGXvYkrkjMkg74nHj+IJe5RZnUf8PTz+VnnjPmSSwo4TuFCTQCj1zRZzjmEOUhVw9UauoVTCsKzsO21nmsuzjwySIZIkhCTnHJF5cDbt4p44MmavAGEICWwjiOfiIcGJ4iRfKdBa4cC6td3BCkpCEbJTbtMt0ZmVFCM8SJSxYaNgxUQJIYJeR4mkcIg3e9AOWDu8RqX3xw1wu0/74bGGlVkxiKJgLn6f3vNWPhSttp65hQ0l7iQM8XTvcBg4vVlvaw7Yjx3oYM9M4CWwBCC5URcDBm2HCMgf5EOVF93hOez47M3FLDjTs7E/5X6LxUjudacVAjMQpNk5n6eQwOZk9HF+TG/dGz4cVcU9NSgK7nNBzSZSBtMezt+PTe9g2mc6yqnuFy7KS0DVs3elMX1Y71bA9ZZMjDdsecjojIodID3zGijiAnNJEkJiST1g6nWV7fTPHXuKmMLAAEqeUSrOSJSDFJdMedgEN20gm4+ceScN2T7wENwnsshLwajGmq07CPcFkOnMpWEK0UEtIEvqRAHhmpjPrMC3rpEWU6SxolJgJ6UxIYBNFRjObMmaYUuP7uAmG0y+CZ5uDJS3muJDJN8uEzsKnM+px2JlOZ6qrnZvR6/KuaQ/bAdlWDmPfPyaLHBLYQhCwCBNo8LI8vxHlJXdPunAcdg4Oc4kbXuIOBLwtvNQmcYZUhpc4OIfEOCBZpCZFfudR7srqRQLbjqyEPDGEBHYZCd4JR2yhaM63TJQb5yFKmZqN4SVe6HvMeCv9vYcHNaxLMoV1AbZ9xs29RQSJlR9BmBwa/UACWwSi/GQb4TgZAV0CWQDiSE4cthHVZeF0xrOdyJzVw1YQZL7n3CpNxUX1Ek+A6U5nis31GcC5z7bFzLzriZyuifLkmQsJ7LIS8INJe2CEGzwKv3Sms8LH6eZgfaEmUspcOwwNW3c60xYn9olTTLnEYyZQig8PnbUmSKgeNlFkMnOJs1iYxDnnxgSvI0lymVrjHKvdwdxMZ/Z72PmMlmEiwdQ9bF3DTmd1K5Q4xS+kYZvp7Eti/YevoqKvHQAwireoezI5xKPPSGCXkxgksi91WUQR+L/77sBZmy9CgqUF2pc7fx+Vs68oY6vS6Hmx09qiO0ObBIaUrRNZppd4AAu1EgsxWWLo5RKY7nRmfJWFx7Efp6i0RSIewscJD/7r3zhvzffTL0hAp9yQ/jtG8wpAAjtiCL5CZxnxrBGmou1TJJiCf484CR1JCbtvfgzy1k/K3SzPpLN2aeNL3691XA9bH5Lh+d4NL3GertYFwDoO21iUwDjec6azEPVTseG92wAAn3zzXLQO/RYAYLvRY2MruOJ632Ih6CqRc44H7roCx3XenX6RSVAOuRgYP7OSuwEAACAASURBVN/pWfQPBt08YZGVPgDArvN+hzVbk9hwzysYXeY2ZcIz/gfg2tnJeT1sZnrNO2ljVOmczvohI9HdjPeX3ohBX2/TLm8jhk196Gq/lZzOctH6onHHyWjY4RDb46IOCeyyIvYgSyocw9reQ22iFysHzcS2viSmdT2DLRtWQXIssNNYxapGDV1gQ6oEQ1Lb7y1vm/Lj/LvIzSXuxInMtOcdIuqqEtjMhmJIciUO/OQS43VW15T3ePPyxSjH6QrSsHPg6cRL+QnXuPILCexIEewDr3BAYhx9lYOwy/9cj6c+bEHvsudzHKqcwMFCWWbRCzLvR1KqQGZJUaEmYYvEKYVziSPL6UyBhMJx2PmKhYRnD7u2UsY+P7wJq1u/TL9WXY2awaNsPumjnaRh58A154GwLfiKBQnsMiJ6qTh171IBZ6qHc9rL2zmljp8VAVnpQ1KuyH5RxEnYR1gXAFsvcWOsGGbkIPqgdOOorqYKdTU7OTo2nxWhUO7xsPLlxs3o27DK+HtAdQUax+4DJKqLcj1maNgWjpExC2UlgR1JAtSwM5yN/OqJul9xlJ3OOOdI8H6kpEoAYt6z20jWzMQpDPoetgQgWfiDzPynOH1QNCJcrSuZUvDefb/EidIzWa9v3noe+NRzi3JNbiewYwb1ghAEswq3T+rgDoVzSODg2sPiyaRd4hzQ5SapcFQiiRTTBbbI5jxtISY5NcWaE6HY1MPO+JTfbQEGu73M8pP/e3bjdKZNxyJaY6CO7VrehXZ5MF6feivu2PFP6OMyUj3tRbumrYYdM6gXyojoGofCOWQo4BnDxI9pU8j93IDpSymoZP1QJM0kbqSsFOeezcU/bI83hSgxQ8O2JjMMLIzVurxjznTm/RxiwsHlauz0rdnAmAPUcVDEBYZRkMZuoSboIidoSGCLQNBaQ0BjV+GaZmM4G+k5pN1cIF572P1JTcOWMjVsiDmhZKQOBdIOPmbSRhLT1oiLOGyAgQXSBWKOozgMb30Jpid4SW+NFHFsGwKbRBVAArvMFCuXeDBw3SSuDRN/3r5q23x7CwtOX0pBJfqhaAI7jTj37HdPXbeU2GU6yzURi9MHRcOo1sUztgIcEkIv8WLnw2GGl7idqApPn/mBBDZhieF0lrO69aBhI20aFX0rwA+qwE6Cy5qGHURaziLBXAiTbBO3k2pdyD1ewD4ICi8RFGZ4hq1DRMwFTfSFG+epwh/0dU2Kw84kugKbK6hc+6zYq9V0+qZindjfWfI4nbk17xpm0DjYDKEK7CrWD242iQtERpJRFcagcCeWj0yTeGFxH7hubWiuoo8j0526GfeCa9g5vgwBLFLsIKezbCLbC4nmFRj05EIkWlaWuymlI+BnR9/DNgQ2/DmNleIBLzf9SY4q9Gdo2JpwE3EOdihMzA5UEtPLaxbQsHm2SdzehB5uzHHYXnKnM2NBFI5+Kol/htMoE0EXOUET2ThslurTfvaWuSUOEFT71MO6jIdFM21yVw+H+6xoYWPtVxvR++m/wHgKmzv6MAbt4PJ2xvvCmYM5h8QyNSW1dVYtTDsbqT8dl0nNHNY+XcXtim+Igq9vWvB709Ot6gsTI8yzqE5nmrndMnFKZHXOvERWYKcRaKLMIei2BRvHyQHDJK5O1H7CugRN0xkAbz76Z5yRXJx+QQK+GtBoOkrAe/aR6Uyx0ZjzvRcWzdEL5lHtz1cjPP1UMg07ZoLZiugKbMH3g7IRc2WdUjgkKMbDYuxghqJPS4ec6gEAvDPzEXAmoaZCxpDtxgHIkIkC9ZllS6xyiZvKazpJoGP2kA5qD1t4fLSTB7zgLgaZ6Va9hXm6xanAFrfPgiS6AltQIZiJfbWj8sI5VIGtDRNvGnLalJnOkCX2fXtl1Ng9cyYWx+bjspDtROb0eL28JrPd7mCm34PoA3Gf66w+5G76VUMThJuf+C22SQPBANSP3RcDDz4/0HZ6xewlbvgyFNUkTsU/MomunUHw/aCiEHAi/HRqUv28QRRxEFV4eYcVcIwRcRuAK+7akuNVjsLx9GmNPPv4KJvE03i/xzG7TMIqeSIUJYkBya0Y3bMajR8ttv9gmfCXl8HhNWzDuuJFhDVsDYG1OdErWXGulteEXq2LuX9ACwmzOGBYFUQUVhnlMgvGVXOAsbQpNK1hOwsDyzqR16YK/qykY81z3nF8jkMmjQcmPWX8/dS1J2NexYoAWhcMOclgDCfUIjqW2u1hCzoeikV0NWzBkxCEAUWLuwwkrIuxyCbQcCRMBFo4cp/9b2c1yG+F8eclHh70u8+1MkQNf06oDqE47Cwi2wuiZw0qBkYcZ0DCQdH3sLM0MbgUPnHo/8L3KOr+W051NysTN7L3Lo3EKRaalRH+k72l6w+BFjz5KMYylAu2yDFiy7Ny0Bfb6YwEdibR7YVQeImL3DYYmc7SXuLuvULTWobmdMbjZRy3NpWWEdP353R3OR1/69RSknHfLCZ72Dytc6rO9D6+e8H6zLy1Uwr/DLtMZ8azJfQ8HxzRFdgx1LDTBKlhq9HYADSrpmDCRxAUi34pidnQM86+S7MJXWKAwpmlJSffnn0Ut0IycVYQJWoE4YRqR5z6057ABHYqlcLcuXPx4x//GACwfv16LFiwADNnzsQ555yDvr6+oC7ljFBo2NYTvScClqWql3h2alIA7vqU6z+YJ6e1cJDPj1pFzHs2mbK1Ntq2MKu8ZgEBzPNXqgpGmIVtwei9vcIt9EyhaiUZ27rFguKwAQQosBcvXoydd97Z+Pvqq6/GKaecgqVLl6K+vh4PPPBAUJdyRbxWvMGSdw+b+3A6Q/Q1LSvEHIcONWxuNoV6jcOOLub9XHOfeT2rSOMm7UhXujhs2+If5CXunubmZrzwwgs45phjAKh7n6+99hpmzZoFAJg3bx6WLVsWxKVcEJYvshjtDOYBSu9ha2Fd+plD5HTW05/Ci0//Hav/+UvjX/PTlwJ9nYFdgxXwu87USYXHyokMJqczzRfKqqBJfpN4QG0L2QTt/1sXd9x48WlxjXHuCO/euiCQOOzLLrsM559/Pjo71Ulw69atqK+vRyKhnr6pqQktLS2255FlhoaG2iCaBNZZDQAYMKAKtQGdM2j0SS2oe66qUitE1dRUojqAcw5o74UEjkRFAg0Ntair6wIHQ6JCdtxmSZtfGxpqkZSq1R1xFtw9A4AsS5bne+XTr7HXJ3/CGLYR/UiAQUHlphSSex8MPnZGINfXhXK+NvTLMtZDjV0eKMg4rK6uUH/WVKKqoRYDaisBqGF3+e6hvjeFLqT7mXMODgmMKXmP70uqwpVJGedjEhi45+9dTqgT9sC6anBB+jET/XsG1LHdL8tg4JAkycf3zmA1rspBKiGDAZAkCfUNtair61Tng4T18+eFzOdZltQ5sr6hFqjPvUZC1sdFlZDjImh8C+x//etfGDJkCHbffXe8/vrrlsflhJDkIZXiaG3t8tskAECioxeDAXR29KAvoHMGDYdqIg7qnnt7+wEA3d196AngnO3belAPjmQKaG3tQmdnLzgYkv1Jx23WK3u1tnYjKXPjtaDuGVAnSKvzbW3vxs4shZYxRyNx5HW465HH8PMvTkfntq4Ax4V+j7nna+/sAweDoiiB3rMferrUcdLT04/u1i50dan+JVbfS3t7DyoApDLuQbe05Du+L6moVgee0Sfc+ngnpJJq1aZtHT1ICtKPmbR1pn10Wlu7jL/9fO+69irKuGnTxomitamzU62EmOxzPh84IfN5VlLq997e3gtFyb1GKqU+e9u2iTkunDJ8+EBHx/kW2G+//Taef/55LF++HL29vejo6MCll16K9vZ2JJNJJBIJNDc3o7HRXL2o2IjvJR74/hQLtniAeQ87jQeTOPOZeMUjKYUjgRS4XMSkfhwZ6VuzSXsPizMSdQO+Gp6VEV/vIkyLM8na5yzrnOmPRtnpLGdsa453fk34Qu1hG/vyAfi0OMR2Dztm+O6F8847D8uXL8fzzz+Pa665Bt/5znfwxz/+Efvssw+eeeYZAMDDDz+MadOm+W6sK0LiJS4yRj3srExn8Nin5ZloUwqHjJSxD58WrEGOC+5gYg7vOMyXtUudtp07nVE4oHtEGzG6b0I6s4Kf+cDNVQuMHyNyQbTeKg5FW7acf/75uOOOOzBjxgy0trZiwYIFxbqUBTHUsAPGWFHrAttLooSMQ+2clYqBwjlkKGCSVnHMaFdpGlGaSc0tutVDn+z0nNAWcdXGOMgUwJLl+LUq/hEIgjqdGZaUnD70lzhF7Pmr+KmG02l/ScMGAi7+sc8++2CfffYBAIwePbpsoVwA4qlhB6w9pjRh5yfTWWbbyuExrZrEFfRJ5qEeXBsYuKVASk9qUcAUplVwHJhj08UKUSo2QcVQC9VnZjN/CTKdUS7xbCJcrUt8DRuw3vsUAa4lTsms1qW94+YsQTfLFUnNJM4k9R6KoaEVnFQd7w+XjnR1JWZ+I//xuUdCYTJqlA7I143RTsWw5du/gjzltPTnHOYqd4TgC+9iZLQTd6FnisMuannNbGuQNWKPj6CIrMAWd7BnkG8mFAiF6+U106tbX7nEy1CtSzeJQxfYxi5QKU3iYmuX6a0O2yON3wbv+yMs/+9QMK6gvTeJg9seBt/0HwAWwzqwcS7wA5OBvo3gbx4S617NC5JSmMSp+Ec2kRXYacSdKFWCeyg9KcAFMO9dliLZf9CoTmdKhoatvRGkVsBDskA0SHuJOz3cvODYb6+9gb32BgAs//RrbHvqmTx9mu10JvKixS/FEl5BVd4LAvMihBXjWTJBXuLZRLcXQrGHLXLb8nmJa1Oui2Znatj6R0s5cac4spzO0gTZBgVWCy91GBY5G1QAFDLnqn7B1p7w5hSV+dJy+hdmTk2jguGjvWKPGBXV4mYXLeCddCRC/n7kCDaUVXSiK7AN4vFFFoN0HHZmWJfHibdME20qlYLEOJgRh13adgipV3qcX20d6xRFOy5/KZGQiVp36L4K5vKavpAg0vxl3FMpLW7aqe2Lf8SD6PZCCDRs/3tc5hMG62iXUw9bf0B95BLnaS+sksBTSfUXZnI6C3BcMFg7D4pddtG705n5NPnGcfZrPid2gZ/jwvgJ6xLLSzwdrpcZDoiiPs+Ghk1OZwCiLLCjvZ4vCaqhl4NlOZ2l/3eEcageClJa4cW5KrClYmY6s7kf0fb9zaVKHDudWS1KjBNnmsSLhZjPtdn6lC8WPWr4srg5xfASp2pdQAwEtkhOG2FDUfQ4bD2si6HUcdR+0TXsYoZ1OWuIgH3msC/sSkVKDFBMKSrNiVYAplprPCNg/xUZER0Zs8userG4ub2evn8TYVHlguj2QlFSUAZL0CtwZjhgBOMEouhx2JJpD9uLSZyxjL9KGNZlCGw901nwC7mCiVNY6R3tbDGND79lEtOfN4+74AWOiEIMSDsXZi9R/H3nwm6lZGW8S/9flEvZhHWZrTtRJ7oCW9AHO0zkT00KeHtAy6XZqtV+mFyhNaNYCzkbhywBcRrWZbvgMDkfWXuJIzYTaxQx+zJIjGle2qUwiYv5DJWaCAtsHYEnCA4EKci4ocUGc88KVKczZvYSd5M4JU9sbkn3sDUNW5LMQz3IOGzrczlPSlJKLJzLrO5DT0lpuSjJ9pBOXyVDE/NbtUrwiTvtgGXqQ1/tFSscMF8cdt57DhDJKitfTImuwA6Bl7jocA7NSzztMOZWw04n7y/PA8e1UCNIxQvrSjvfWL0HCLlwZGkx7Kh1lnHYDEqGpmVM6aY9bGjvxoFAwroE8xI3k17AFy8OG+BqjL/V/CHoAq5YRFdgh2SCEDmXuLGHnROH7Z1Se0xzpfhhXU4QyvnRZVPsTOLpXQYtDjtfrI9xjNd+EKj/8hDGLIBuScfXe1/Ae70qoRJdgR0KDbtIK9OA7lnh2WFdQcRho8Rx2NBM4jxHww6yEXZxTKJO5GnTprM2Wt+k83EhYj8ETxC5xEVbABhfb5ZlprjCVNI25giVGPSEOAM+P+KuHo3EKUbhDBW3ZjqljPfINaezYuYiztI6zO8x8ZzOLPeqLY8vPErN2mV+jVysPigGQXt1c5PXuWiUIqwLnBfMchY3Z8bICmxeFE0qWER+GIGMPNxZJnH4ejhKHuKkm8R1DbvElhdmXErEcWgagQUznZnjqtNIYFqZxfQHzNqX74lVcKcza8LW3sJkZzor/vOsatjR6kM/RFZgpyfm8jajlDBJ8xIP6J65omSV11TX+24fnkDcbzzDDZO4biUojkm8cBy2YJpSHichZ71R+B4LxWGHYQHtB7PHtFU+dZdnhZj9pQtsbT4oqoZtXVgnjkS4vGYYvmTxHCpaO7rQs3mt+seWzQAAJklGBJrvfTVW4knIMInL5jcCu0ShkKf05cSbeM1x2FbhgKoJvXD7edb/hYSVeP1QVHxaBPxlhwsWc3w9Y9lV2ooBAy/smBuCBFlBEmGBrROPLzIIuvtTWHHHT3G89BwAYKL2ulRRgxS8hSgxSzFQIswm8RIvkNKmYXHHofN9dut9+rzaYL6wLt8LF7EWuDrmetgCrs98o9vKOFN/lsIznnFyOsskugI7FF7iQeMvNWl3fwoNvA1b5WF4f9y5AICBNZXYbtIRAPw4mWSbRktb/CM7tSErxlYJL2ApYeJF0hpLKBfaX6EjjXrYOaUlMz4lppwVHLE7zTolbZDXgE3oK2nYEUH8L1K0qVw3eSlVg7DbISenX9d+mrUIJ5TdW1gxhXUVwYTGCoR16Rq2aN91PqxixdMpai00bGTvYecvFuK339OjUETyZbRT+8C7dpjlqCeAs525vKbhZ1jEoc0orCuLyAvsUiasePfTdZDfvweSVtJRZgw77X04Et/4luVnxEycYi19vDygZd2nV6z2sEuDv/zrxUH/NnRrg/N4WjsrQvY98qzf4+cEGl3M46bIe9gksA2iK7DL4Izw/r/+jp/13pj1WnP3KuD4B/J/IODFBPN5z3qIhtWnPT2gJidxjuIuojZ19OKjRy7BmJ4PAQAze7aobxjlNfWGBOt0ZifwWAgElaXTGQpbg3JDuAod7zOsS1CMEKc8ufO9wlnmCrn8C3tzK4oRh92fUrBuzYfoatsKABiMbQVvncVs6zO6ArsMJnFJM782n/gm/ttVhcT9C7C9ruGFAb3Ig5Xp09MDWlpz3jtftmP21vvBmITN0lBAAj6rmYwBg3fRjtBX68GMixwzoYlMXUQU0olTtNYxh62z+BrNe9j5x1FAY0BIi1QudjXEnZFpEvfdJP9wgLH088yMkR/c2P7HU8/g55/9yPh7kgy0SU2BnT/sRFdgl2WAq3t4icpqJPqr1L3LAg4ZjsKBXOD3sbEz3Xp9QLO1z+KW49Pzn3fvOh+DZlyW0QYNfbIpVeIUATOducUu+FCPx80d63mcziKaS1wld2Gme1RHi/TMFXhYV0cLAOCdnc/AttoxYAzYYZc9UBncFUJNdAV2YGEkbtC9byUwBqR4kWvFWrXC4z2ntQJrbdHtA1pqZytzDe+iX8/433paFm0P28hh77Bsqt24gCm8J/9+tj7J+w3zC4f4C8SIbXSpGGPHvFhnDFB4sCZxKP0AgO2/NQfJxj0cfEB85+IgibDA1indF8kyTI3pZ61wyINI2pcRZ1loYvb7gLLiCnG7iZIF/IDr1kpe4KJh8RK3paCXOGAlpGF6NYqkLSlB3p9Y+7Pc+Jb1rZTg75ml+tRryOXRqXv6U3j+n1dj747njdcqKqsw7KirkBo6rixtyiS6ArsMqUlZhubibDAH7HQW1APucmIu3CYnk3hw6CZxZuUVbly+xJOgj8u9/8UWSEt/jkHKFuO1gSN2Rv0RV3va0+WK2picjxYYN4UWHEY9bD0O27By5NnD9msSD8kedpqwtdc51o52Ps6pCWyUSWBvaO/BbluexRjpc6ytGAue7MWErg+w7as3hBDYEfaXL4OpRPe5ATMcccLgHaxj60DlZUXNS29FKOQ4FzRpM2FBV1ZfGvaqjz7GQd1L0dT/OWqSbRjR81/s/Pl9QLLb8znN7fMX1qVbk6w3CMQMXwwOhmxHs/Sz5B3R8q+bF2LFyHTGlF71WnKVszYF7CWu32PH4AloOu1JPD/hSu2N4iWHcUNkBXY5Bjsz7Q1ybdfX+ngxwjUysQ/f8RKilBnyU1zzsK5hWwvsgM2MXJ+yireHrZsiaw/+JYb85F94KHGEcW1fOBSidpdJ37/5wHxe4lF2OsuH9+dbNIGdJvt5DrJ9sraHXS6TeM4CXLPUcRLYRaYs8Xm62S5jLizl9fWFglenMxTWTr2tqEs70PU9ZWbldMaCDetyhs9FmSkMK/0jmHtwvNXhMNyvoHYZ1Vzi5j6AqC31Tj6nM/WNIDXs8prEwfU5UGtPwBUQ/RJdgV0Wk3jaS9zY1xNudWyNk9hRLytqbt7LLOLoV++hgIYdsG+DE5O4f6uCbrlhxvnSV3ePvufotOhnuvpW/uMlsJx7pGpdQSCW01mOMEPwTmey4tbpLB19EAS5leb0+gxi5NOIrsAuw55Z+otOe4kXisMWbfLSB6vtfqaLCaTU34JqEucoWVhXXgcrMz4ntZyPFmcit7LM2C7kjOgB01jP6hN/34fh2CToXnjOtghHVpIRzyfVTyYU2XvYQW5xSZpJHA73sIMmJ4RRXyQLsmiKrsAuw+o0/x52YYJ0xvGdmtT4mLXp0/35TceWJKyLW2qPLOvIEuLnciaTOPf7PeeJw3a0tHIYPZDf6cxtKwnRjOpm7VP10AnWYpbgfUhBdrzgDiwyRsO8Laifnyti7GFHP6yrDF7iANNM4oU1K7EexwwsJ+Zc06cTsvNMB2tCy7kW55AZBySrPWz9AQ/mAeTQNQ2bYzKO+HJrO7auehQJpcdo0o7jv43KkfkTRRir+xwpWJqxbbcfaxU9kL1s8juxiqHhWMKssgAG8JSLduuGl3jwWfxkpR+pRBnzmpk0bKbXIBDkS4iuwNYoZbUuxjPjsLUJiyuCfNXOKFQS0Esu8dJnOjM5aJWMAtczLYCeW/owfr7511mvfb1uJyinLrc4gfmefC5G8+YSZw6eFYs9bJYdwlg4M1qYngbv5Bfebs8hlpe4eWvEsbOiC2TehxSrCOx8blEjMjJN4vobYmjYETaJ65TSJJ7eZ0ubGQtcnwfsS+pzz8tJalKPbk7pazh2dfKGojtUWWjYDPp2RUAmNAfxtjnaZ1LVrP978E14e85zWJraC7IWf2p1BpUy2WRsxoV6iGogVX/P1yc+NWwxZJYlxh626f78aaBi2eAy9E71/wylJAhSCkcFTyIludCwA+4is0k8HdYlxgCMsIZdjsHONTO4ai4Ken+n2OiDtVDBAtcTUKnv37hefoEddKIF7WSwMRpnaybatRtG7Ai5fhd8hmqb05sdrqTs1z20VmuW1TumVwsvSizDezItCwH5aoiUyrcQQS0I1ZOJNocw0//e29fR04+Na1dCSv7/9r493o6iSver3ufs80pydghJDtGQ8IgoDwmKKIaHJCaRxEAQ8HpFRhmZOD4ueh3AF/LzMaPoT6+/uXfmIojKKDpXLwgOBHE0GIIRxCuPICgQSSA8coAkJ+d99tm76/7RXdXd1a+q7tr79Nmnvj/Oo3fv6urqrlq11vrWWuOo2zb6yH7YKgJbM5jS4uW+1+tCy4vWFdhTFocdZlAm+zeLswClaYtZMp2F2LMN9mHDDoZAxUH3a6FEHvRNfrnYdpF0Jh7PC0njbQq3gWvYETuCopl3dcPjMWi8P26V1Sr+s0MwiTO/fZ7JdMvtP8XlL33KO1AC9pVfm7k9bWDvOo/DLsQTaGGBPQULRHBnhtSXWb9/N98mRTR5RbWeaYI2cU/CGNDEspry5GWcGhQil4JpzBYf00T/cUjDzmtejvleXFgXku+R+7AD/s2Yb7Uq6UwApXDDC7O//EXazAM+/y7xr3EkdbuZBDIxCADYcfznMNL9apQIwfFveAtGpFvQXN8egp+esdWNht1gTEUcNqVc02ImcZKW6atYc9JFjMCWtBoEET6zkaLUSyEYcw+WZpa4hH83ZJ73Jdhx/4CKhq1vMxq0BsmeH4VoMmL4/NzaYoHjsEXXhz4Uc7OiJZe4+868+rgzUT/0WABAe6UbGBjV0MNs/Qka/F3OS0HCulqYdDY1JnEq1hhOgPalJ2+yEIrEZA/ZfVaiSbyB4JWo4sZiClKTEvF63qZCzc3AbKR5NWy1xYemvRfCPUQnWsn53AtiklSHBtJZQe6dc1zcflnQyNMpyEaMa9jcJO7/ZOrRsgJ7Knxmlk+bttgiNgWTLV8u8QRtMYPPqulhXdzcHC9cdF9PhiUeJJ35wv/ABFxSA64gFFKT5h7bcH3N6MunNcPOERnSxP+3XqtA0RDatCD/8ylshTPOoNbhtxetR4rfbki1Ll9/Wq34x4svvoiLL74YZ599NtavX49/+7d/AwAMDAzgkksuwZo1a3DJJZfg4MGDuTubCc0UmBSg7pB65uPoB51WynIqIBaoF+H3Wam1G0E+atRzcXP+xoV18dM0T3A1f6O3qZCJbY83IuvxB6v3PwiLOO89CQgrIPhuF0tbnF4oxphFx2HndANMWd6EaIgatjUl5OV45BbYpVIJn/70p/GLX/wCP/nJT/DjH/8YO3fuxPXXX49TTz0V//mf/4lTTz0V119/vY7+ymMKMp0R2IFdcbM17Lxp+tLyYmfzWTV5U8K7Fn1N/rHu55KgDYUWNa4xWz5hKUE68y6WuZuJ7STsC5JziTPLC2OJJ1kdso27P8dBUUH5D7hjhpyPqljCIkzIApB7jcv3XP2rrQ44G3n/PerlvORFboG9YMECHHfccQCAWbNm4cgjj0R/fz+2bNmCjRs3AgA2btyIX//613kvpYjmC2xmIAXAU5PKJdUoBtLMeHGmzySEjK4NITuFzgAAIABJREFU3khR29WwY3zYPDewzpKAUkZj339+YlxsSksfBJa4bjNgWmrVwIlRhxFnefEd0VwlzWAqEaTY5TP9F0vDBoLWIUII6nRqXJtR0OrDfu655/DnP/8ZJ554Ivbt24cFCxYAcIT6/v37dV4qHVwuNFHDpTa8B52sOeX3/TQAov9GQNZyes3dlLiksxSTuLarce0z4R6Zo9r/JcDzYaeOqbio5dz0pFghwqdTJcsLH42AtSkvCjZXIhBtHtbx7hfk3mlwJmthiRfMciLOZzY/i+LD1hbWNTIygssuuwyf/exnMWvWrExtlEoElUq3ng7VnAW7q7MdHbraTAGBY+asVLrRUa2DwtkRRd2TzdjMMZ9nQVe3kyGos7PNCY1QxOzxOsYBlEpWZJ9ouercEwHmSLdPQYh3j8RV5yq9XQBPrJ8P/v52lJ02u7o60B3Rx84up2xfV2c7ShrG3RpzygESK3rMHDgLOfu85O4l5vT2wJ7dzZeHuO+3tzv31NPTie5KNyx3MzJnTicwR/0eOjqcad/VXUZnpRs9PcNOLwlFb0Qf2OftbW2Rn9vtbbBB+Hsxe9LGAKGwfGNScp/1nDmdQK96ny03HG/OnG6gSfNZGa7MqVS6MWu8jipo7FySas59zr1zuoDuqb/nnqEqCCja2kqoVLpRGq+Bus896z2WXEE9e04Xf64qY9Zedt7lnu5y5HxXRc/AOAgoSu3OPfb0dMAGQUe5pKX9vNAisCcnJ3HZZZdhw4YNWLNmDQBg3rx5eOmll7BgwQK89NJLOOSQQ1LbqdcpBnTF39UnMB/A2PgkxpoU08cIZgMDo5io2aCwQKkdeU91m/Ldm657Hht3ir+Pj09iJEObg0Nj6ABF3Y5+DoNjk+5uU+E5UWcPzc5ne/GBgRHAasPulw/i4H03oGx77R1+zJvQ87p3SPe7Uunm7Y+NOTm5xydqmIjo48REzTlvfAI1DeM+OD4JAmcDFjcmTO9kn9uu2X5wcBx1exTMDxj3/cmqsykYGa2iOjCKuquhDx4cg22r38P4hNPe6NgkJgZGMTpSTXyuw8MTIAAma/WE98JxRwwMjGJocNy9T6+9us36PAqbqvfZrrvfHxyDjSmK0U2FM6MHBkYxNDSGDiB2LsnAHTIcHBwDrU79PQ8Pj6MbQK3urGkj1Zr73KPXOBmwuTA0NIF6yWnDP5/TMDnpfH9kZAJVDfN5aHgc8wHUa85zGxtz5sbERFVL+3GYP3+21Hm5BTalFJ/73Odw5JFH4pJLLuHHV65cidtuuw2bNm3CbbfdhlWrVuW9lCJck0aMSbxmU/zy1utxwoDnW+8od2DRO7+A+tyjM16TeixxhFmVRYcM6UyVZOLP/haFe367BZe/8K3AsZH+n2BUQWBHXzjmmpotb5QxjVJJZ/4DXvpUqTjsEJM2Xy7x+MvEu2+SEp6ILHPv/AiWeO75UAzTaRq0zv2C+E/FkCfPRZan0by9ahDc27II+Ga2CG9ebkffH//4R/z85z/H/fffj3PPPRfnnnsu7rnnHmzatAnbt2/HmjVrsH37dmzatElHfxWQvEDsHRzHUc/fimPHH0Sluhe948/h+MGtKD2zNfsVqS81qUS4Dj9RG/IRqqLDcaKukp10FmK+2o62t2/9D/HC3+3ED+prUXKPZYIb1pX6ajd1ERRJZ54PG0gn+3kuZ10+bGEDQCC38ObZyBVhtWswKOIVhGyYCuKsBHjIE9xiR/l92NnFoebUpCEfNsun0SI+7JNPPhlPPPFE5GcsJntqEf0g6zZFCTYGe4/FnPfdjn/f/hgue3htrhR0BOHUpEmks8Jp3ykEKo9So9rvBO3TXeBKbWW0lztRI2VYXOhmACuvGZvpTC9LXP450tDfbJFyFoT4b5IIARtus3HwKhhFQ4yIiEpewyxP1R+egxppc9wIx2xE16rPic3F9SJb56cIspvfZBRrlxP9rsvUUU9qtFikMyBoZSRsU1KQ169lM52lpW+kFLAI5VoOsfJltKGUujmyg+zCtFzixTC0OPCKt0dD2mrA2ouIxw1loBMmrE1KsJBdYNO0BYBoNifTsEk44qKB/On+TGfO77TwP7ECWfK7nQ65imZhJJv9vTjs8MK+9KQ12D5rLZ7tOQF7Ol+LdnsMXc9tVbw+CrWwh9GgvhXGJM7WB08p8VdpywLvPSnGcxUrzbE1j+ZRIjSidYt/pJiTbOcx8NzfnoDPKLDdn5T4HzRA4uZaA3winlaZc4KrJAGRazDhk2CxDpuUcmrY7nebvrCrXC9sEk/2YQevoTvtbnrPgwURQt9n73qE9sVw+vITgOXfBeC4ox678QKcgAmFXhbDJKmOHO/hFCR/koNPKaF5RW1ODVuztUkMYdSaL10DZqyGbVM39zcXsK6GndEk7rQnatgWijfZ4sF6H5uaVNEy5G1i/MdiFiFXeDkatp3dZ8SbTU6con2CJ54TfBdJyAqQMb1j5uIf7A/Rtx6XRjfZ7C+Sj7jwjjNyZKir7r9aURG4Jw2ksyJZ3xgCNEJFi1skYt7FqULIh834GQVZx1tXYKcszNyEzbRSXqg8q6CgPiHNFiW/Bimcjkb4sHPuddnLGpvRSm2hjd59B4UXG2/uz3U3TrBrCj33X9NtL86HzS3iOn3YSLZKhD4LWhXSxjTkE+b1iDVp2ASgVOLdibnHEPkoReviZxZEayk8ijZOQj3sfMIsr0lcr6AX5zPnIpnymg1GionF04iZgHUFRUaBLWrsgCs0pmKu5a7WFQ1e0ES5/YRnEUq7yQR2VrN4mn92Knbywc1jyIedSmqJNzVnAh9z8Soxm9vIPoggERp6vID3FwuRQTH0rzSIxLv4za9aq8UQ2CFLi0xa3VRk5VMI0MpJ8drSw4TXh9YV2AyxpDMKp1iHQDrLuJOi8JmI/MfSfG9FItHQFF8lZBdv8LNCk1nwOYmkE5s4tApCs2nYPNtE6rhq0rAFE1okiO9k/7Ulw7rYBom/W6H28kF+OYpzlYilJZNTmfJiIVmeQYGmS+NRrJv13lN9GjaJ+Gsq4bm4hHtslbCuIiPpZeIaNjd9uItnRsIT2wAIXp7YhbgRSVXUC18G4fke0wyaqu2qaNjuK+maxB985mXM+eVHULEP8K90zT8CPe+6zqeh+ptLNrER3Sxxdl2Fs8ICOG3RC/u8Va4aak2xpCFbvuJqiauSj9gi6OQtkETRzMIRiNqE6PFDF+ze3VtiSUViibUyiLH2yPdFNyfFL6o9k7jOYkF50PoadgxEHzbLVZyHJU4Q9J3KkM4aQSzJnDglzfeoShaKZMKzcY4+Tq2gSfyxp57AqZP3YRbGUC11obM2gKV7fwEyGZcmUDQ3x1ynqXHYAulM6GMa814UsLqrdXkXiu1AYv8cs6Hl4yUkWx3yFY0ohiaWhhSbi1wbBavFHMpdl4s86LXqtpajDX2gFCDEp8ixn0bDbgIcNk3kR56G7ZHE6jQ7ucCm1PFh+0CBWH8vM6EXE3o0qbT2AEAMw7JdHzax3fzUNSfrWe/Ky9H5mvPwk+uuwsdqNyJ2kaBBQle4J5oHnW1KUkLhxCMIfEfSfB+jYY9Ua3j8vtvRM7qHf+OwBX2Y+4YLY/oVXCTlU0wmm7jZRiRNWGUjnRVDaCWCRPA7ck3yYi0QURsxKbdfAsIRE0WBQDoryPvX2gI70STOTNiuhu1qCZTamaYJE2QBdnKqqVMzeBh2Rg0bySZx5cQpEe3FxxCLJnFHkNOaE6tLS2X38xRLiJD2M75zup+LjMCmwWszNwDiN3aB8xEt4O9+8hVc9Ogn0UF8KV2fBvYfdTLqlSOleh+8jnAYKT52xGnM8ZumzJpZ4Rb2aOh1eRVDWHCI8zlX95gjIaOxV7MVgrcSyKdRHA27tU3iUTteFzRCw7ZzPBinvaAPO4kJ62UBK84CJGYyEqFKMoletIQJJgovyxXYjHRWd5NrlDqE78cJ7GQN23vjdU5wSZO4e55nibF8v5MEYozAdsdusm6jg0zihdf8LXa+5wH8S89lzuf1amKvvTAxSddM4kYOni+TPfeE8/2pTFsF2t1bXBjpbTYPwiRSTc+xKBsxYc0yLPGmIkHDBg2QzryqLFl92DTCh52iORUQfs9N+MNsYRxpXnynbdefazGTuKthu0LH07CTX1lGYwrHPjM0pliA1GIdwxIHSRlT0WwoEG1YbfX2rlnonbcIEyW3Hr0mrUCs0iTCmWXepkMmxa3qRnW6zaMs8ySqDfGvYiBoEs/Tv8KlJkUwwoFbg0wcdjOQYKZkGjEjnRHiEmeyx2GHfTkymc50vqj5hFF64hRFDRsRTXGljmnYgkYssMQJ0xJdgZ2qYdtppLMGIUlD4B+JVgWPdJaE8GgHBXY4sYqsOyDGtx5xNDk+PzpJUGzGPGZbyjTXirGwR0O31aBYy3P0ninnPef0YevmpPDNqWgSLwha24ed4EO2mUmaaXSu6YNmTdjBfNi+SUZTzUXF2jl7C7Ok6VMa8S98OMSJJbBxnwPzYVuuwOZhWXGLfcoCwEzvujOdJSJ4huWrh+20kfKexC1qgluBECcVEM9nLykQpbdgacQ61h0EF72oZtR92MWaK/LQsNgXxLoQFV9PQdBeH8WeG84HALSVLPStvAxkyenSrToohlBka2CgTDI1PuzmIY4lbgMlH+msxDXsbJNDNLEDEgvxNINnFpX1YYf99GHSWbQPm7g1sUUNW8zLHb6o6B8W70EzSUWCi+D12TsSpDYmE/nEAikhQWiLfntxjMM9imwnMclQyngp+jKz5qAukrYjImAe1lLcJ+05ThW8Oxt71enYZS3F7MmX0VN9CceO/B71x25VaCufhq3b9y1aGb3iH8UQ2K2tYQNI92HrI52FfZnxixJb4uN9rRmQM4kA732scpqF3Ztyrqg9WkGTOFzBLfqwCezIltPKaxJrKhb88CYl8J6k+bD5edGkM2YSJ5ZbxFMxOYy02S81dI3yy6anuM3v4215FGxvEvVczz//AwA+AAC4Z+crmHvX26CyPuRKuhJoR9e7JGR7zGQNahxaW8NOicP2B8hzNmDeetiBXOLTS8OWSrMJ9YU2uCkRNVzBJGYFw7qIQDpL82F7cZ1pr7a+5yI9Hm7fwtEEagsCN3lzASmUFJUNfVOQCEmkM4BZk/xx2PH3w/kiLQfL5+IJamr5ULA1JNbVkSVmOSWqo8kQNyV55YJutOKs8SGJdOYmOrH8GnZ20pmn6/hZ4s0V2DoIGGmLDOU/0hEl+qloBRCzeFnBXOKWLZjE00JdUhOnsPM0+7AlSGfEd8/BaIKUJxcbhx2z6Qn0TgV5xoREfD3+GQTMx0qXKcbC3hzodd/khTefk55rRuGW+blqNonzn0yRI5ndN41Ai5vEkzVsC97C6fC5s4d1ibnJ+fV9i9JPf3U3Ln7i79GJCfQC2GjZGCKvznS9JORJTeogTZNSbT9pUokm8WBqUiawKY/DTq6qFmJMh7pCYFP9EzBJ6Irm5kjXScKY8lA10YfN7oFz2FiYmGzlObb8Jmc6o2yXlrKR829IUjcxiKrulXR2MRbMRBAg/ByzC5TC+etlXR2ZMtjlvdfGbcCLlDilpQU2TWCJMxN2Hbp82FHkI+KW8HQw9vJfMYeM4qG56zDcdggIITjmlFWZrtcIpFZZAqCSvU0qcQoXsKwueZB0JmrY3vDGmcSD9bVDn0v3Xg5yGa2CGrFT9CLYPyupjZBfPtheyCSeiphrpfAtElsk/jjstIU9Tw7qggmxGGRL4RvbmraW9CDJJG6hpCCwi56a1DKpSZuJ+JeAx03zOGzkNolHscSdP5zjxCVSLVnzCdQPPRYAUKl0Y2AgrpCFGvISqmSEjwpZyCtVl9YiwLU9ITWpxUhnVrt7dhqhSs6HLZnqJBViOb7IcwSN2F/WFe6RJISSS4TSMbrtMqZ9BtJZfkRZXhI2TaoCrSAmySREvlF5Bldz2s28SJvP2dxNOTXsnETbEFzFi80lz8xfjGfQ2gJbQsMmgcQpBFbGOGyPJW55+g+xfE5fwoUQJ1Y1CrlfrmRTpurkiBaNTNsUCtiXnLF54Z7rsP++27Gi/ohzyZJcHDZ3acQI7IZNQKmF2ROwwbrpwY1d+GtxWggN/PIyp7H2kkln4QiFOA2bLWLxmyC/KVRMPiFCNSf9dITKxjYexdI60/gaFgFsSlBSuG+vMnox7tV7bkyBYKSzYryrrS2wJXzYXlhXvkWEJ2Lxv8xMtlGmyTPTZSnTNRqNtAnpnBNFLoo5V0aLEtpauOhIPPvMQiwaeRSLRh4FCPBi7xvQ5kvj6fyK7gRJI50RvYuD3OsiaNjUhphgJ9PazgQk32R6PmnndzMXGZmsfg5yVUAqnOnUQ6NIpsXz38dZThhBS8FKyTebxXiu4UxnBM4R48NuEuIEdpAlXiIEdWqhlINc4EysUuCI0wV3oWb5sa3GCOzcrzw3iaeQhVQXkMBkFLW04A77HW84BnjDH+EvW+F/SVPNvdI+MX2LIHEdIkrnk+ARr09RGnZyYhQehx2TazzUXMTxNNKZjNnf05aSz2dWDrWyjEUTWslIK6QjhWLIMA7HAJTwHPhmuIkm8RAnJh/Ed9cL6yrG+9faYV0JcdheRhtPw7Zz7KQ81rnvmJDbmwlsni+7Ycj2ckn5YxXbD2U647KEBn9L5/5OM4knC2wuVrRPwKQFR3gPENSwIY5JCMI9CWPFxbjFcpOnpW+Nu4yEfzK2hz6BnSKssqe4lenJVEI3Oamg95pkElcUbnzzX5BbpYKl1HOhFUPDbm2BjXhzEst0RjQV//BY4hFkIrdNwkziVjGHXcYkrpKCUobERiD4sNOQ4sP22osZ40xaQDykiHrCrTlXjyKdpfVJaIj7jAWTeBpZSdjUyGc6S/lQluTGf2bRxKYbNEiigmh36W9nBpN4bg1bP0STuGGJNw3xA01pkNXt+bBz1MP2ZU7jl3c+df51WeK0URp2zhhjz+SV7MMmlGLk4D4AQFuJoKNnbrrf2+skP+r/JT9h2XnZsniRhM+ygI0WJRKt+nzY/pPT86OLGnbwStQWrBSZ2cUp1qikOyTBjUvSxi9XWFdBfJ2RIAjEIOftaSjJ0FQjZXNKhPVOtk3hy2rQPEY85wDf/DK5UIxn0NoCO+El4D5sgSWeJ6yLscS9Y5b3IQBCm8QSbyDqpB1lOo6lN53Ij/Wf+HFYp10ROlcudEpxh50ijLx62ClhXVqKMwQ6JvEZ5dEJYkY89lc0gmMUdz4L60ttL0Nq0jTY/nrYMlYHpJ/TEsi1wSjo5iSB/a9upSyWhi1aGS3XapCtFKx+TF/JIQUSu2bZ7qJCxDjsjIXK7Yhc4iGTOE9wUVyTOIDERWbJ2z+GrTuPBAHF/pEqVr18I+yDe5J9K4m5xNXqV6eRzuRY4vHfV4VUJSu/D9/9GZlfPVXAstOFMXTfK55rjQR95mlILwIrkVAnspUEqwsPeZSDriIRjUQgVE9i0yLfcDFuPj0Om/FD5NsM5RhQhl5BL96jx20qxjNobYEtEYftr9ZFc5DOuMYQYRInrt7XaA2b323WCS6xyCx/7euA174OALB9134Mbf4JrJhNDpWYvV6mI9lOMqN2ikk8kXTWiN18st8fAECpF06I8HsiSzoLhW0JxD3Ph61YMzyRoJkOlq3Nmwvx5zrnFENrKS6KZRJPi6/nSo9Cf0ncu5ilcxogbrTyukp1o5iqnjbE+x7q1KmHHdSw8/iwaWghpgI72KKNDevKCzlNyoMlS1byI+RzUjWJp4V1yWrsmiY4khcx55xkk7j8wiz6sKnvp3dcOdMZ5J4jSdGw1aAaHliMBTMJ0XHYLWgST+S4QG0NbYB7RgeIb/NrNOymIkZrsG2HJGZ5Pux6LpY40yqEhBjOxYK/i5o4JWUHLYLAzWwUW4iDWRf8m5igOVc5l7Cs9phoEo93lSiDIjk2NXAu9TLiRaawjf6aV7IxRiMWEqdwK0RspjMEzvMOJ+WkSyad2a6Ju/avJ+E4ECwg+/AKjos9v+VziaMVTeKetTAKXpIRlf7m1bD1vw9E+GlIZ01D/EDbghkxb4A8Z50HXqCgcLKaYhIP14KSRZSATbwecyMkCIb0lpg4kDX2pAhsibhuQS/VBAkNFdThOhCKyPC/2D4FFzXRcuN97moFik9fR/a3uSe9C/c9eZCbuV+0CF590geSrloUpUUjIoRVLgW7WCZxDp1x2No0bM0mcbc7prxmE5FUrYsTdYgrZgkBhZWZDejsO6nAThZN4jXnhS4q6SxC+0uCvBsh3F5op64ahx37XJPjurWnJoXMJsevTUeMcZoPO4V0RsVFL5V0lkzMC1+fLWLx56889TTg1NPk2oNmDbSA0FNcpljWBK9eQhw/RN3fq82HrQli8igCZ40rCt+imJJDG+IFthi7yoVPzkxngWpdwkIcynClGbqFURp4spm4MZbwiaexukNtpvpn5TT2rHXPw1dLFzr+eFoeThjhJkgVYL7sS8EL0MB1Qq4YWTRRi/CX41RCQRb2OCSWSc2MomxsUralmfy9+e6NaLZChHOJI1duC91obYGd8Hax7FDEJYDlrocNBHZmwQ54JnG7Gf7rzC8XRYxISPmaypiJE0xth03SMp2ladhoEEtchpDFfdiIKa+ZYhIXSWcxoXG8Jwr51pPeGJr1vUiEKumsGAtmMnzjSaPWg4woiLDwkMYPUeuvXSBLAmeL+DKd5Yke0o2WNokn+bDhati64rC9MDG/qTMoXJomsDNCdZFJS+caNCzFXtX9LWsSD2qj45N13HfPnSD7ngYAHDHycOC80Nehd+mP5i7Enu2eL2jYsqlEwXzYgoC3RYHO3jG1dzlOgHp+Pc2uhEyCqDiLe8NRMGtCWmw5m1vqG7EC3aewBjLLa1HyALS2wE6Mw2YatiuwQVDP4avwYlXDpk6PEV2HjcYJ7LzF/TgZS9mHHdNexAQXhROfCJLXFAtbbHnyFfyXe/8WXcSr72XDAu2oRH7fuYw+E5fUAsXv2SvDmknDjqnG5RU8EZuLc1UEr+uxe5sJxXjdwmmZYURabgomdLUgMeWspTS3kljnSn3R9Hpwa1IoisNo2E1AvDAJJ5tgvorsmc6IL9Upvz68l9JCHbQpGna2t1dOIxa/kz5mwd6IwknNhy0mBRmr1tBFqthz5EUYPP4SAMAhlXlomzU/tedakeR+8f2VSFJLS5wiSmR+PvvNcomrZTpLuz7rsyr7PBFEVRObPjjw7bdhiQ0sJi9jf66WUiwvTQbf0CeSzgAV4abNbaAJopXRY4nXk7/YJLS4wE4w89keSxzwcomr7OTv+NWdeMOu/w0LNg61KZaQl3AQr/Gu4RMulFKUqF1okzhc7S85QYYHK8W/w7XPxPYUWaJi4QvqFFTp6l2IzsWvlWqCOl+Uu55EY+mbHF9sfkSCHTGaIPIigI8II2x6RL99ioldOfa9AcjMIyiwxtr1unfgoV3P8oyGT5K5OPTY8zO3Jxut0SykiVbPRaYwt2jO+9Q8ROKaxfzyRdlctrjATvJYuiZZZhK3WFo9+d3h5JO/xIl4GI+1HQeLAM90LEPvMe8Uru9cy3Z9l2lFKXKB5FgIxYYkkCl2XTBhqcZhUoEXQF3OASmVpKaURyLRg7T8yu5V3ZN9qUkV3oPwGMVp2GzrQITvpfcuaUz4Qq3Vh221HOns7JVrUamch4GBUU0tBvkaRUHshp4ZNJU2w7q4ETpdXB4cdUSfCy0vWltgk/iB5jWEXY3X2x3WpJqmlKKjPoyx9llY8KFfRp/jE051m6INxWaJKxNG0pj1VIZdHHRNpF8yKLDtes09rjiuuidg0qLDP/KZxBPC/8KI5gHE5RLPHocdT0YIlY7NC4KMOQ+KpXU2B8UQFswCl2wST1KSor6jJ2JdN9iml1dxrI3jlRf+CgDonjMP3bOiOTKNRmsL7ISXh8Vhs5KEbCclu4hU6xQ9GEO1rSfhLE+41ClFidhN8mFng2pqUi+XeBK5iUYc8/6KTbsZC8EczFwbJblXOcuikgQ1Zj3l1b2UcomHiHmChi3GsivGYZMEqkejkGVhn3EopEk8/jlkyQqW/7lqtlgKm9NyycIE2jF/fBfm33omAOAVVEA/+ie915VEwwX2tm3b8E//9E+wbRsXXnghNm3a1OhLekh64TmJIFs97OGJGmZjFLW22UkdYBdD3aYoobEC29t65NWwJQU2iNQmJ1kYq/lTmUmccJO4q2FLFlTRLZzkRss5ozY5gUkyjjbUhfvNRy7yaoAzgR3WsGt1G7TuMunjCDSSrHIdyEphK6I21jgUi3TGkRAyqZwVjGp6phqjPgDwe6x0t6O86gvY9uL9/JzOQ4/EEVqupo6GCux6vY4vfelL+P73v4+FCxfiggsuwMqVK3H00Uc38rI+JJnEBR82YX41W2pB5wK7vSd+j0dEgd0slng28NrO0gQwZ8ySyzKK7QUzlanWw/VY4i4flZnEJfOzcw1b+yKY4AN2+7bw/5yFhQBQAl4ix3vfSM3WFLRChERdiEQW1LCf3jeC6o/fjdOtRwAAl/N+TeW7qLixLJjMai6KcfNpZVazZQUL5iSYalAxpwGA5ce9Hjju9VPTIQENFdg7duzAkiVLsHjxYgDA+vXrsWXLliYKbCD2ZXc1M38NYcYSlxLY1TrmkVHY5cWxApv6Fs46pWhrsIatA+omcUAtRlEUTqoatjt+vAJaMGOdPHSyxJPbeuPbLsRvHh5HiU4CcKw5Ryx/O8q8iRRNShTIPp+480v0SQfHeO/gBN5InsdzHcvw9KErAQCH9PZi4aI387MTSWe22jOSA2lQGs8WQsFM4h5iNOwMWcEIkJMlrneMqOJ61Gw0VGD39/ejr6+P/79w4ULs2LGjkZfkGJ6oAWM1WH/9LXY/914AQE9XNxac81XQ7vm+ggnBXOLiG3/GAAAgAElEQVSdo8/j8e+/N7X9ct3GUtKP4fKx8Se5D/2Fm/87RtGJ5dZOUJIWH5wdzNxLaTYjkzMk8iZxoiPTWcg/m3JN4Yu0rmYSh7uoUG0mNOr0JaH/xy55FbDk0+ldQ/RmMWyFEPKpC2PIrEbcze/mLyd9x+N17/x8Qg/iTOL6BWtgk6KyOBZ0IW0oCmIS5y6zlCyCalnBdN2brg24a/kjxWRNNFRgRy2KSTG+pRJBpdKt5dr1kSruazsFJ078AQvGn4ZtUxwx+jz2P34iJk+4CF31AQBAV08nOivdmDW7E/fOeyteGXwRC8aflrrGQNuhmHPCOnTH9Hnha07BU48ciTnVFzEHwERpFnD0WszznV8qWdruubu7AwDQ0dGGngxtdvc4Ol+53IbZEt/vnahjGAQlK/q5jRGCl0BhWQRz3M+tkvP858zpBCrdbqlJoFLpAUrlUBsi2jucc3q629Fd6Ua53Wmvq6cLnRJ9ntXTAQqgXC7xPuXB7FFHay6VSpmfIxOwc+Z0ArPDbTD5O6e3G6h0o73sTNuenjK6K91ob4f7f5czJh3Oge6uNnRVutHVPQwLFG3lMnoj+jh7eJJRACPvobvLGfPOzrLUGEvBiaNEpdIFmQgBtmxUeruAsqY+NAA653Op5GxCZ8/qANU17jnQ3V0GAUV7zNwZtIFhWLAsRL5nUWCcmUrGNbGjXOJ969IwRl2dztzq0Pmua0RDBXZfXx/27t3L/+/v78eCBQtiz6/XqbYYxhKAU//2f/H/H3jmAPAfb8cRv/8a8Puv4cPu8dEJYMK95jkXXQHgCqXrVAFUY/q85q2nAW/dFjruv8dKpVvbPY+NVkFBMDFRw2SGNkdGqiAAqpN1qT4ND0+AgqBeiz7/4OA4AMC2vedqu+bVwcEx2Bjl2vnAwBhQSg+pm5x0zh8ZHkN1YBQT4w6RanTc5s8xCaMjEwAIqtWalnEfHBpHLxxSV9b2mMV58OAo7Hq4DbvOxmwcNkYxOem4AUaGx1EdGEW16v4/Wg38Pzo6gYmBUQwNjaMEG5O16Pk1NDyOeQAojb6H0dEJAMDERA3jmt5VtpcfODACSFhH2OZ/4OA40F5cLVvnfK7XnXd9aHgcNW2x3dkxMuK8B5OT0e/J0NAYAMCuy60fAADqhHVlXROrVTfj4ciElndzbNRZTybG9b3rMpg/P4m87KGhAvuEE07A7t27sWfPHixcuBCbN2/GN7/5zUZeMhYnH17BH978P7Bn3yP82NzZs9B3xJop6U9DkYNtrGIIIiSdFRo2oYn+WkWfkZhHW+AiSEOXSTySWKcKOdJZKGxLCI3z+BhBJj03iU8pyUwA55zJPYdiGigbjNRSss1HkovLK7crl8uCtVQk5v+M9mG3tbXh6quvxqWXXop6vY7zzz8fy5Yta+QlY2ERgjefciaAM6fk+tMBTPgQ2SQm0FArlssi1cQpwThsyLLESbEWCADe4hA7jGLJUCFsKxSHjcDnadnV0hdNtXzvcihoyFIhMT3GyPNhq5BQU6jnMhfVihnswwaAM888E2eeaYRkM6BDGKWSxHzgseuJucSDMbdiaUgSYjindTA6Dls+5SubiPriNvNObRrxlx9E/IsPIbsT4XucSc8EtpMDQMb0HIkGFCqyU/Onx6Bge62ZBE5KTdA+VdcffcU/dFnMxM1xsdDAxNYGU4eMJnFu3pU7nyfGz5RLXIjDzmgS5/XLpyqsiyPPBE8xfYrlM2NC4/imRYxV5881RsOW3ehpDutyIPsciqjvNBgFExppotXbwKs8q2KmJi3qzrDFU5MaZIO8hp1UXlMubaeqhiUU/+A54RVN4tqqdYXr56pD1ofNThc2PYJWIIa+MQ07Ld+6f+P157/uROmFPwAAuvoPus1rNBPyW1ZV34u5kDYU08Rt4G3g5ZJPAWoWvaQW9KHYY20Edoshz+vGSWeyMdEEiSST6PbYFPV6aisZeoICW0yAk9pn6PVh6zCJhxKhiB+L1bpCJUaDnxMhuQxLi8vjw1IwNF7Dgc2fxbml3wEAVrDWu3QWPEjz2wso9jraEBRN82TzOY7j4glfhYdFdWnYml4Qvvct1tgzGIHdisj47qalHhSRjWQitiG/GwfgJQXhPnBHw5ZNs8mSy+inlGjQEtKqdXEN2v0dYto7Y8MyR1FuhXDGOY4lLi6xY5N19GAMr3QcjufP/BcAwKzuDsxZ9Dqlu0qCyGUwiEBqytrmwrOYRUM1WySg5oJrBryeF6hTPhiBbcDBKEyyu0svjCN6enryx9ee0LQy6UQkXNlqJvGIzuWCaoWzyDakhVdcWFeQuMcq0LFbrDPSWaoVImhCp+09WLTsDVL3oI6MLPGCaj4NReFM4tHPwONCyPc3t8VLNZwzDYZ0ZtAs5GaJS/mcg9fLXdydKvZZzCXOw7pkTeIk3xgJ0GMSlxNeXr8FAR8q/sHcBs7Y2LaNEqEJGjaB/5nXWGU5yVC5LAjV9E7/RsP6UlRkq2fWWBD+IwxWvU92PeDFhnTcp7ZNjRfWVUQYgd2SyMgShxqBynmpJRKnBCakKJyoQkiWN5EIJ1wxDVs2l7hm0pm/4ZzfjRNeISa9IOA9QwbzYTMN29OYnQ9SxtnHKndKwTZyechq7i3mQtoQKG9qphjc3STnIvPe3gI9U24RL1CffDAC24BDjtXtgRVMiQvrYu1RX3NUNOdmXrCDpLNGaoNJcIRizkUn7asi6UwQdnzTIn7ObeJuRbO4TY1gmanbFG2kru5mUAB/DyT5D9NGaDUExbj3tPVB1bzN3Ek0115Xr2AVCZxFgxHYLQRt5l6FylnqcZdCG6osUSu6vKYKS9z5nr5FMP+Iq5HOYk3o/HOXfMZTk7oVzUppVginvbqbyrSxpWAz+rALupDOBKSlLvbCPCVN4sjAYUlsTUczjcjqpw9GYLcgaEbWNptAsr4zxgpVK68pLtRqBexFc69HElFgiefa0ic0nBGi1SHiDHYR4Tf7mBkX3SxwltCezfxyctO9bju12zNnRlOC3EI7EzVsmpZQZ6oQV17TtdTImsRZDoM8SobumcxY4saHbdBw5NN11Q27Fp+gOTRsQKmAPRHisImiSdwbI50s8bxaQrK2yesLE1Fgu3cSYra6mxqbhXUl1wwX3xsnbrveYDeDWXrkUQyBzUzYccJMlYSqVcPWNES8J0ZgGxQelDr1qaVN4q4+HpvpLIoFKmqTaiZxMcbY07CnqFoXdGh/aRp2nMYi8AB4atKgZkbrksQ8Mfd4A0lnNM6sn4aCLqQNQcHuNW2jy9cDyfmgp9JdY2A0bIPCQ2Qbp8HLdBZDOkP63tkR9goathUsbAFFlrjn59esteSa37LCK8gC9/Y8we+FTOJMsMeEvonhgDwzWgNJZ+os8WJomc1EI/gWepBsEpd9VpR/J09XDOnMYJrCeXdz+FJttZfVcnNLp2Y6S6iHrezDYhq2zUziLA5bMaxLEzz7QB6WuKwPO+b8EFEmaIWw68wkniaAPdKZSirTTFBkifu+qL0rBpJI0YjZXJYtBqQ1Dlvbhq4Bpek0wgjsVkTeHbkmDduZ28KEDAknqjRfiSCMeGpSaQ27UT7sPEgW2HwB5Cxw38X932OlR8Wa4e4YeZp3MpgPW7bGeBZQYeMm8YWZh4KaZeNAGEtcSegVKw6bKy0NzUGQHSY1qQGHnyEpsz6q7qij26AeG1bmfFeT3veXrXj+xWEcO/b/3A8UGc26zYw6Fte0sC6+sAm1pAWBzoh5HumMPdfo6S6GA9o2mmASZ9eWzzs906C8qWkw0qxJFgFsKp+fjYlqFdKpCNIgnbOoeyUjsFsMuaa2ov/GStlRM0JW0AQd1CZVTWLzemfjFToHRx/YhqMPbAMAjFvdoJ29Ut/Pnb41qs3ciVOSvxuX6SxU/IP1ga9hLKzLrWiWZuJ226tRihJpdFiXqg+bfa2gK2lDURCBnWJN4u4tSTeHnggL1pgeU3bImlUwGIHdksg6wdVeesJ21HGZzmT6oZg4ZfWxr8LQ0j9gsDrKj7160XzQ0YQv+aCbdKZH+UnRpGjwPCIKOzH1KLc2uMZ/mz3XGIEtEH9sXo6zcQKbimb79G80rC+FRUGFRmy/GKdFmnSWnIhlKkDdNbCIedwBI7AN/ODyWtaHTeQynUVNcOrTsBXnxqzuHqC7x2u+3A2MSkps4fraoCFxStrixTc2Hn3Y+ZeV0RQEOhfU7PNUAeySzmyKNtRRa2Sms8ylI4u5kDYGWceoMWDCOE6YsVTF0ixxmoF0Krahe1Mjm3d/ilDMXhlkgkf6yAauEUuX13RziceZxCVMXs6Ebd5rSARhlxeelpBn4ZBkiYuJU0KkM1dgC2FdXglSyUxn1CGdpbPKs4PfqeTGKQ9PYrrCC9+bHvfOt4sK/dUVh62LCeG1UsyNoRHYrYjME9wjnckgywT1T069YR3y0BrWpYMlnrIwe+0zDTuFdEYIbEp8LPHk5DKilsPjsEtNyHQ2TYSRgY/PHZvpTNLixtrT6cPWBT6XprYbcTAmcYMwFMprihr2bx54AIc/9q8ooQZqUxxNnkeV+AlhnnCy3QnbiNTe8X1mxJjimMTlNWzhWlT43MfhpXBC3wgA+eQyQhx2gcprhoh3MwLFMonLZEJUisPmbJJGRliotlPs4h9GYLcQcvuDMryslFiB9aT/4Tvw7skt2EMOgw0Lw6UKyOFnIUpUeASVqZgc+kxoSaEuUkgzfYZIZ8IHArvfcVVY/DgzicfVtxaZ8yysq5EmcdEPbxBG5vStUwmilpoUmkzi2lHEPsEI7NZEk0zizjeCGnbJrgIAOv9uG9DeFT7fT7DSUK1HFflKlTQakj7sUJawIFGG36NqvnXqkc4cH3YDWeLGJD7tIOPfVU5NmnM2yiYDkodaaGuzYXzYLYTceXkzhDKKAtuyJ50/SmWJ7zLSSRNJZ2AmcU0NavHDqfmweWIUflyo1kWEhdNWy7fOTOKN1bBVzb0zUbDLRQ80C17q4oRzEkioESdr27Br2/cJ1qyiwQhsAw6aYXcpCr8SraIOKyGG11uobQpYZKoyXWlmiecxoYkkMvFj4bhoKiURJnEK4qsZnswSF8N06nUbJUIbTDpzhZFCwgu7oItoo0BSNnJThsR3XX4zrCfCQi+KnnfPmMRbCA4JzMIhT/07Jp66BQBQa+uBfdFm2LNfld5AqK6y1EVDJvG6hHYNTA1LvGGks4YibSMlsMTdaFkef60YW8rrZ5NS45av4qzRBpKQSV1MibyGncY6l4NmnZPxPowP26DReNPiCrYdfhkWjDwBAKgefAEr69sxMLBLTmC7UPNhWwENsI1Oop5kSg0RaaYirEtvW00jnQlx1t4QuosM64kbH8/lOCuQEmP1IAhqr7y6V6mtgfpGQbXHIqGYMiMRmepha2GJ66qyZVjiBk3C7M42nH7O3/P/b/yPO7Byz3aQ2phkC+qhMxQWN8nalDoCmyRp2E7b4yMHMdp2AGXUYtnLjYCzGZEnxqSBUjgVyfJkOpMN6+LnMcIWI5UJpDNuEncFtWziFCb4eTnORqYmzRKyVMxFtHEIukqGJ2rY9chvUK7u52csXnwkupe8qTndkag1oOKPZhyWQm3ZhM1x0WAEdgvDclna0gI7A+HCbwKr1mx0kBrqVntCp5xX7rA7/ysOA3BMCdhvLZW+nj4UaJlIS5wSkRjF/YLzv0A6E6tvcT+xJOmM1xpvJOlMmSVeoOfVbLhjdNvvHsJ/f/ySwEe1R9pw4EN/Ado6m9ARGS3WkucluFEieYSjijVQCooFkJoNI7BbGKTc7fyelMuzTTOYJ/0msMk6RRmTsK14Dfv0s87Blj8Ood0ed/oGgqXHvRXNWG6c6+kNI9MhRhi5KM6UKLLEPUs4I5Uh8DkjnYn1sOOLNgCUEtDxg3jm0W3oeGmP004TSGcqIzjTRLboR60P7gUAPH3S5zA4/xQ8cvdNeH/t/4JUh0CbIrAZEuYPkWe1U/5Tx3zUZTFjm2MtzWmHEdgtjFKHI7BtSYGdKcsPIXxHXa3bKKOWKLCPXHgIsO6/ybevGTz0TVtmJBaapsGHLRmHHWYPh0llFMD4cw/jmTu+gdcceML9PFrD7movYS86sGD0cSzY9l5wA2vHbMUbUUAoljzl9Bknrv2z0BVt4wcAAHOXvhGzF70Rd2/fBtQAqzqEevf8xneILw/x77otcFqSwHzYzczDkA6jYRtMEUrtTkWr+oSkhp0hcYoNC+Aatp2qYU81PA27gAJAcRPBtAEiEGUWzu7Ac3Q+jht9BEufeQQAMIl21HsWRrazcHYHBs/5n3h46AmMjTtx9JWebhz6mtMz3IQssviwZyjc52xNDDj/ds4FANjlWcAIQCZHpqxrYchHYOhgiet/e4zANpgitHU4ZrJ6VU5g81dUaQJ5JvFqnTokMqtc0NfdA61NYHB/PwCgq6MD7T2VbO1oiMOmSI7DBoQYZFeTHnjoZrz4+IM4fexp97jz68RX9WJs0xbsHvcW8s6uLpRcF0kUli09ApXKcRgYUCxTmhey1boKFq/bFAjvVFt1EABgdzrvKi07FhBSHWpKd+TDumQ17PxhndzatP0bqG6/1vl71kKUL/pZPr++IZ0ZNBud7e0Yo2XY1VGlaEU1IgeBxQW2jTKZhF3qjswdXgS0lyxU0YZF++8H/v2N/PjAO2/C5JK3KbfHavpqCeuKv0qg/SMXzccjDx+DRRN7sWzC8Wvu7T4Gpc5D+DldHZ1ARzP9mooo6IJYLDiz9vnffg/7HvglThvf4SiwHW4xnfIsAACpDjepP+lRJCphXUB+V8epxyzFXbsvwtxJZ/NtjfbjTcM7cGDfX1BbuFy9QUM6M5gqdLZbGEMZVFLDphleVpsV/6AUk3Ub3ahJpSWdKhw9vwcPnPIFDO97GADwwsExXLDvWkw+ez+QQWDrAB/thNSk/k/evPRQ4CNbAucUdYMUBx7Kp+AGKJavs/GYf9gSvPjMPCwZ/COW4I8AgL1zTkTJnV9WR3M1bDnIu5t0mMSXHtqDpRd9jf//g1/egzftvAh7f/UNvNx5BOqUouPgTlTqXihcZ/dszLngetCueTG9ytenRsII7BZGZ3sJo+hE59O/xHPfeSL1/NNqE84fSnHRzov9wg3noscGjiIvYKi0JENvmwOLELzlzW8D8DYAwOieATxz289QefTH2POX+5TbO7xu4xAMYn8ek7gbPjX7tve4nABgf2keXmxbDABYS/cUdgHJDPee5/zkbC6IXyktQH/bosjT19A9RVV6GoZ1Jx8LnPwI/Ntt/8as1Olo2Ae3fhP7tv2g4f05pVZz/0ognZESLEpR+RdvDXiufQkOWnPD51KK11t/RQ1LtD3ao44+Fk8/dRhed3ArXndwKwBgBF3Y1b4MNixM1us4dvD32P2D8zAQ0ae3cz5AMV82I7BbGMcsmIX7e96O4yceQmddwmxGgJ2dx+OQw06WvkbbEWfg8Wd2oKM+gg4AL7Qvwaxl67N3usk4rm827q5sxJtH7pYbowg8Uz4avUe9PXMfVpy6Er/+3YfQYTtLc3WyhnlDj/P+jLbNxbOLTseszFcoHt60Yh3u/sNzPLyvWq1i/vDjsc9gtG0unlt0Onqa2cmC44Sjj8Rvnzwdh9ZfyvzuqqCTAE91HId5h6+IPedVp30A9zzZBctl/0+MDWHR6BOx/Xu5/VXoOmoDwnX9smHFUfOBj/0BLwvHGd3yhYPj+I+bL8drJv8c3ScL+GvHclQWvF5Tj/SC0CzBtw3C5GS9+aSXKUal0j3j7lk3zBjqgRnH/DBjmB8zcQznz5cLoTTVugwMDAwMDKYBjMA2MDAwMDCYBjAC28DAwMDAYBrACGwDAwMDA4NpACOwDQwMDAwMpgFyCeyvfe1reMc73oENGzbgox/9KAYHB/ln1113HVavXo21a9fi3nvvzd1RAwMDAwODmYxcAnvFihW44447cPvtt2Pp0qW47rrrAAA7d+7E5s2bsXnzZtxwww344he/iHq9rqXDBgYGBgYGMxG5BPZpp52GtjYn98ry5cuxd6+T13jLli1Yv349yuUyFi9ejCVLlmDHjh35e2tgYGBgYDBDoc2Hfcstt+CMM84AAPT396Ovr49/tnDhQvT39+u6lIGBgYGBwYxDamrSD3zgA3jllVdCxz/xiU/g7W930jFee+21KJVKOOeccwD4i0h4kKkAVSoRVCrxJQBbEaWSNePuWTfMGOqBGcf8MGOYH2YM45EqsG+88cbEz2+99VZs3boVN954IxfKfX193DwOOBr3ggULUjtTr9MZl5JuJqbh0w0zhnpgxjE/zBjmx0wcw6akJt22bRu+853v4Nprr0VXl5e+feXKldi8eTOq1Sr27NmD3bt34/WvL2YydQMDAwMDg+mAXNW6vvzlL6NareKSSy4BAJx44on40pe+hGXLluHss8/GunXrUCqVcPXVV6NUmm4Vew0MDAwMDIoDU61rijETzT+6YcZQD8w45ocZw/yYiWNoqnUZGBgYGBi0EIzANjAwMDAwmAYwAtvAwMDAwGAawAhsAwMDAwODaYBCkc4MDAwMDAwMomE0bAMDAwMDg2kAI7ANDAwMDAymAYzANjAwMDAwmAYwAtvAwMDAwGAawAhsAwMDAwODaQAjsA0MDAwMDKYBjMDWiHq9jo0bN+JDH/oQAOCzn/0szjnnHGzYsAGXXXYZRkZG+Ll33nkn1q1bh/Xr1+Mf/uEf+PFbb70Va9aswZo1a3Drrbfy43/605+wYcMGrF69Gv/4j/8YWXO8FSA7hi+88AIuvvhibNy4ERs2bMA999zD27juuuuwevVqrF27Fvfeey8/vm3bNqxduxarV6/G9ddf39wbayLEMWT48pe/jJNOOon/X61W8YlPfAKrV6/GhRdeiOeee45/NtPHEJAfx+9///tYt24dNmzYgPe///14/vnn+WdmPsuNIcNdd92FY445Bo8++ig/Zt5FH6iBNnzve9+jn/zkJ+mmTZsopZQODQ3xz77yla/Q6667jlJK6a5du+i5555LBwYGKKWUvvLKK5RSSg8cOEBXrlxJDxw4QAcGBujKlSv5Oeeffz598MEHqW3b9IMf/CDdunVrM2+taZAdw6uuuor+6Ec/opRS+tRTT9GzzjqL/71hwwY6MTFBn332Wbpq1Spaq9VorVajq1atos8++yydmJigGzZsoE899VST7645EMeQUkp37NhBL7/8crp8+XJ+7KabbqKf//znKaWU3nHHHfTjH/84pdSMIYPsON533310dHSUUkrpj370Iz6OZj7LjyGlzlx/73vfSy+88EK6Y8cOSql5F0UYDVsT9u7di61bt+KCCy7gx2bNmgUAoJRifHycH//pT3+Kiy66CL29vQCAefPmAQB++9vfYsWKFahUKujt7cWKFStw77334qWXXsLw8DBOOukkEEKwceNGbNmypYl31xyojCEhBMPDwwCAoaEhLFiwAACwZcsWrF+/HuVyGYsXL8aSJUuwY8cO7NixA0uWLMHixYtRLpexfv36GTOG9XodX//613HFFVcEzr377rtx3nnnAQDWrl2L++67D5TSGT+GgNo4vuUtb0FXVxcAYPny5di7dy8AM59VxhAA/vmf/xmXXnopOjo6+DHzLgZhBLYmfOUrX8EVV1wBywoO6Wc+8xmsWLECTz/9NC6++GIAwO7du7Fr1y685z3vwbvf/W5s27YNANDf34++vj7+3YULF6K/vz90vK+vD/39/U24q+ZCZQw/9rGP4fbbb8cZZ5yBTZs24aqrrgIgP4bseKshagxvuukmrFq1im9qGPr7+3HYYYcBANra2jB79mwcOHBgxo8hoDaOftx8880444wzAJj5rDKGjz/+OPbu3YuzzjorcNy8i0EYga0Bv/nNb3DIIYfg+OOPD3321a9+Fffeey+OOuoo3HnnnQCcXeYzzzyDH/7wh/jmN7+Jq666CoODg5F+LEJI7PFWguoYbt68Geeddx62bduG66+/HldeeSVs2zZjKIxhf38/7rrrLrzvfe8Lna86VjNhDAH1cWT4+c9/jj/96U+49NJLAaiPbytBZQxt28ZXv/pVfOpTnwq1M5PHMAptU92BVsCDDz6Iu+++G9u2bcPExASGh4dx+eWX4xvf+AYAoFQqYd26dfjud7+L888/HwsXLsTy5cvR3t6OxYsX44gjjsDu3bvR19eHBx54gLfb39+PU045BX19fdzMBjimpqRd/nSE6hjefPPNuOGGGwAAJ510EiYmJnDgwIHQWPX39/OxijveKogaw3e+850ol8tYs2YNAGBsbAyrV6/Gr371K/T19eHFF19EX18farUahoaGUKlUZvQYAurjCAC/+93v8O1vfxs33XQTyuUyAJj5LDmGP/vZz/Dkk0/ib/7mbwAAL7/8Mj784Q/j2muvnfHvYghT4ThvZdx///1006ZN1LZtunv3bkoppbZt02uuuYZec801lFJK77nnHnrllVdSSindt28fPeOMM+j+/fvpgQMH6FlnnUUHBgbowMAAPeuss+iBAwcopZS+613vog899FDLk1QolRvDD37wg/SWW26hlFK6c+dOumLFCmrbNn3yyScDJJWVK1fSWq1GJycn6cqVKwMklSeffHLK7rHRYGMoIol0dtlll1FKqRlDH2TG8bHHHqOrVq2iu3btCpxj5rMDmTH0433vex8nnZl3MQijYTcIlFJ86lOfwsjICCilOOaYY/DFL34RAHD66adj+/btWLduHUqlEq688krMnTsXAPCRj3yEkzQ++tGPolKpAAC+8IUv4DOf+QzGx8dxxhlncD9ZKyNpDD/96U/jqquuwo033ghCCK655hoQQrBs2TKcffbZfGyvvvpqlEolAMDVV1+NSy+9FPV6Heeffz6WLVs2lbc35bjgggtwxRVXYPXq1TF/Z2UAAACQSURBVOjt7cW3vvUtADBjqIivf/3rGB0dxcc//nEAwGGHHYZvf/vbqFQqZj7nhHkXgzDlNQ0MDAwMDKYBDOnMwMDAwMBgGsAIbAMDAwMDg2kAI7ANDAwMDAymAYzANjAwMDAwmAYwAtvAwMDAwGAawAhsAwMDAwODaQAjsA0MDAwMDKYBjMA2MDAwMDCYBvj/Kc5pFLu+vBYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data[\"EVTRQ\"][43500:44500], label = \"EVTRQ_With_Outliers\")\n",
    "plt.plot(df[\"EVTRQ\"][43500:44500], label = \"EVTRQ_Without_Outliers\")\n",
    "plt.legend()\n",
    "plt.title(\"EVTRQ\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data[\"EVDDC\"], label = \"EVDDC_With_Outliers\")\n",
    "plt.plot(df[\"EVDDC\"], label = \"EVDDC_Without_Outliers\")\n",
    "plt.legend()\n",
    "plt.title(\"EVDDC\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data[\"EVRGT\"], label = \"EVRGT_With_Outliers\")\n",
    "plt.plot(df[\"EVRGT\"], label = \"EVRGT_Without_Outliers\")\n",
    "plt.legend()\n",
    "plt.title(\"EVRGT\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = data[data[\"EVBOA_MIN\"] < -100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.to_csv(\"outlier_358272088715191_16_Jan.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V+vWvz5Qk1dbWyuPxNBvkffv20zff3Ca3260DDujRbN1POGGCFi1aqIMO6rnX6zwej7p1667ly5/WIYcEexL69euvt956Q6WlO2K6lyRlZGRKCs/w3nMPw0EHHazPP/+00Xj4F198roMOOihU3hWZAOj11sV07zDTDCg3t12TP5RJwe81bNq06Tr22GF6661VuuiiczVnzt/3OBxgB7rWASBBxowZr3vv/au6dOm6x1bbzp079MMPW1RU1FmDBh2j2trayCxqv9+v+++foxNPnCiPx6MVK17QH/5wrZ544hk98cQzWrLkf1q9+m3V1tY2W5eLLrpUF110aUz1drlcOuOMs/T444ubvbZ//wF6/PHF6tevv6RgkC9Zslh9+/ZvsnchOztH1dXWli9OnXq2Fi1aqB9+2CJJ+uGHLXrkkX9FWv7BMfjPJUmvvlocdc/sZu+Zk9NOnTt31csvB2fzm6apL79c3+S1mzdv0sEH99LZZ0/XoYcepu++22Dp+0kWWuQAEIfwGHnYz342RJdccrmkYHfs3/52l2bNumq3cjNmXCSHwymfz6eLL75MHTvuJ0m67ba/6O6779DDDz+kQCCgIUOG6qKLLlVtba1Wr35bv//9nyLvkZWVpSOOGKg33li517FfSRoyZGhc39fEiafo4Ycfava6/v0HaMmSxZGJbX36HKYff9ymSZMmN3n9SSdN1F/+clujyW6x6t27jy655HL94Q+zIrPHf/vbGerdu48kaerUabr++j/qhReeazQJ8KijBmnRooc1ffpZu012i3b99Tfrrrtu18MPPyS/36fRo8c1OTHw8ccf0wcfvCeHw6kDDzyo2SGI1maYKbDbfX29P6EbUvj8AWVmZarix9i3hs1xO6WMLMnZtiY5JEpb2vTDbjyLxtrS89i69TsVFcXWfZssbIDSGM+jMavPo6nPdqwbwqRli/xXj3ygM8oe0sWuZ+IqF/B00E+/Xh0MdAAA2oC0DPKZx/dU/9VVqirdT+91mdbs9Z9urVAP/3c6qfYlOcu+lX//vq1QSwBo2vLl/9OSJf9p9Fr//gP0u9/9YQ8l2tb7232/fU1adq1L0n4vnCd/2Q8qPeP5Zq+959Wv9dlHb2qp62qVnTBf3oNPSmhd2oK21H1qN55FY23pedC13vbwPBqzo2s9fWet++skZ2ZMl3bLz9JXvuD2gs6y75JZKwAA4pLGQV4vM8Yg757vUYWy5c3sIGfZhuTWCwCAOKRvkPvqJEfsLXJJKnV3I8gBAG1K2ga54ffG3CIvyvPI6TC01VkkZzld6wCAtiNtg1x+b8xj5C6Hoc55bm0wi+So2BwcXweQljiPvHXPI9+TiooKPfXUkmav++abrzVjxsU688zTdOaZp2rhwgVqbo73ru/9ww9bNG3aGZKkzz//VHPm/KVllU+wtA5y05ER8+Xd8rP0uXd/GTLlLN+UxIoBaMvCh6aEf02bNl1jx45XcfGKRtc1dR75woWP6ZZb7tCcOXdJUuQ88uOOO17/+c9SLV78lGpqqjV//t8j5XY9j7w54fPIw2I9j7w50eeRS2p0Hrmk3c4jP/HEiZJ2D/JEqays0NKlew/yurpa/fGPV+rss6frP/95SgsXLta6dWub/QFgb+996KF9NXPm7jv37Ulzp9UlQlquI5ck+epkxrFLW7f2Hn24paPkkLa+t0SleYc1W8btcqjnfjnyFx0l05PfktoC2IX78yfk+ew/zV8Yh9rDzlTdoVPiLsd55C0/j7y8vEx//vNsbdmyWW63R7///TXq1at3o/PDJWnatDN0551z9MAD92nz5s2aPv0sDR78M1166RW71XfFiv9T//4DdMwxP5cUPAjlyit/r8svv0inn35GzO992mm/iLznBx+8p//8Z5HuvHOOampqdM89d+qbb76W3+/TeeddqOOPH6XnnntGb765Sl6vV7W1Nbr++lt0ww1Xq6qqSn6/T//v/12tAQOObPZ5xyp9gzxQH3PXuiT16pSjFR8Vyut2qu/6+5svEKWm71mqHHlnvDUE0Abtutf6tGnTNXr0uMh55Icf3m+P55GbpqktWzZr9uzgudfNnUfeq1fvyHnkxx03QvPmzY3sOb4n0eeRV1VVatiw4ZFDR5oyatRYLV36RMznkS9cuEBS8Dzy88+/SEuWBA9a2dN55E8++bguu2ymDj20YSOt8HnkTz21RIsXP6I//vE6PfTQPPXu3Ud//vPdev/9d3XLLTfs8WQySbr44sv1zTdf7/Waps4z79q1m6qrq1VVVRnze+/p+f373//U0UcP1p/+dIMqKir0m9+co5/9bIgk6ZNP1unhhxcrL6+9Fi9epGOO+bnOOed8+f1+1dU1f+hNPNI3yH11Mp2xd62f0r+z+hbl6v2K55RZt6PZ67eW1+nBt77Xovz5yqjd2ZKaAmhC3aFTLLWeW4rzyJNzHvnatR/qlluCDZ6jjx6s8vIyVVbuOWxjsafnK2mPr8dj9eq3tWrVa1q8eJGk4FGqJSU/SJIGD/6Z8vLaS5IOO6yv/vzn2fL5fBo+/PjIoS+Jkr5BHsdkNyk44e2wwlyp8PCYrjd3VOvDNzNV62qvTF+N1VoCSBGcR96y88ibmn9mGMHhBtNsmOTn9XpjuocUPM9813kFmzdvUnZ2trKzc1r03sE6m7r11jt1wAEHRl5zOh1at25do/PMBw48SnPnPqg331ylm2++XlOnTovMIUiE9J7slsSTzDwZTkmS1+GWfIntRgHQNnEeufXzyAcOPFIrVvyfpOA4dPv27UNnhnfR+vXBM8e/+OLzSDd3LGeOjxt3gtau/UjvvvuOpODkt7/97a7ImHhL3lsKHmH7xBP/jcyCD7/XrrZu/UH5+R108smnauLEU7R+/RfNvnc80jPIAz4ZZkCKY9Z6vNyu4KOtN9wyaJED+4zwGHn41z/+0TChbeTIMfr2269361aXgmPk06efpcsvvzhyHrlhGLrttr/olVeKdeaZp2rq1NOUmZnZ6Dzy6NZ39HnkzRkyZGizE+OiTZx4ivx+f7PX9e8/QFu2bN7tPPL+/Y9o8vrweeTTp5+117Hh884LLos755wz9cAD9+uaa26SJB1//CiVl5dr+vSztGzZE5F5B+3b56t//wGaNu0MzZ37tybf0+326Pbb79bDDz+kqVNP069/faYOPbSvTj/9ly1+b0maPv18+Xw+nXPOmZo27QwtWPBAk9etWfO+zj33LJ177ll69dWX9YtfnLnH97QiPQ9Nqa9Wp/mHqHLIn1Rz1G8T975Rauv9Ou7eN/Ri0Xz1dPygnVOLk3KfRGlLB2PYjWfRWFt6Hhya0vbwPBrj0JRWYvhD4yBxjJHHK9wirzXcMuhaBwAkSXpOdgsFeaxbtFphGIbcLofqlCnRtQ4ggfa188hb6uuvv9LNN1/f6LWMjAw9+ODDNtWodaVlkEda5DEemmKVx+VQjTIZIweQUBMmnBzZRCYV3z/RDj64117Xk+/r0rNrPZD8FrkUnLleYxLkQCKlwLQeIC4t/UynZZC3Rte6FGqRm5kyAj7JX998AQB75XJlqqqqnDDHPsM0TVVVlcvlsp5H6d21nuQgd7scqjYzQ/esjWsnOQC769Chk3bu/FGVlS0/PcsqwzD4QSIKz6MxK8/D5cpUhw6dLN8zLYO81VrkGU5VeUP3qK+RMmNbSgCgaU6nS/vv39nWOrSl5XhtAc+jMTueR1p2rYfHyJO5IYwU7FqvCgTvYfhZggYASLy0DPKGFnnytmiVgi3yynCQ1zPhDQCQeGkZ5K05Rl4RDnJmrgMAkiAtg7yhRZ78rvUKH13rAIDkScsgb2iRJ79rPdwiF13rAIAkSOsgN5O8s5vb5VCZj651AEDypGWQKxDanKUVutbL/cEVfgQ5ACAZ0jLIDX+dpNZZR14b3hCGE9AAAEmQlkHeasvPXA7VKHgPWuQAgGRIyyA3wl3rSd4Qxu1yqFahVj8tcgBAEqRlkMvvlSlDciR3h1pPhlN1ypApgxY5ACAp0jLIDX+d5HJLhpHU+3hcDkmGAk4PY+QAgKRIyyCXvz7pM9YlyZMRfLx+p4cWOQAgKdLy9DPD7036ZjCS5HY5JUk+p0cOWuQA4mCapr7bWSOj+ke5anfEVMbtcqigXaYCHXq2yr9xaBvSMsjl9yZ9n3WpoUXuc7iVSYscQBxe+PxH3fLcWn3ovlBZhjeusjWH/VKVo+5OUs3Q1qRlkBuBVgrycIvc4ZGbIAcQh5KKOuWrUlmGV192m6KSjsfs9XpfwNR/P9ii6zusUKdta1uplmgL0jPI/V7Jlfwgd7uCLXKv4VY79loHEIdqr0+5juC/G4WHHa/8QyY3W2b1Z2/rU9cWjSr9nxTwSw5nsquJNiBNJ7u1zhh5uGu93nBz+hmAuFR5/drPFdq8KrNdTGUO6pilj+s7y/DXyVGxMZnVQxuSlkFuBLxJP8JUauharzPcMmiRA4hDdXSQZ+TEVObAjtl6p7KTJMm186uk1Q1tS1p2rbdWizzctV5nuCXGyPdpj72/SR9++LauqZujDNXHVCbD6VCn9rmqGPs3+fc7LMk1RKqprveryFUneeNoke+XrRe9RZJHcu5YLx04Jsm1RFuQlkFu+L1SjH8xWsLpMJThNFSnTLrW93Fvbdipw2o/VS9t0JqsIfIZe5+DUeX1yVdXrTE/rVFGyUcEOXZT5fUr3+UNBnmMLfKD9stWuXJU696fFnkaScsgl79eSvJZ5GEel1O1yqRrfV9nSgdk1Uk1Urdp/5YysvZ6+WPvb9K/X12j952XSPyQhyZUe/1q7wie1BjIiHWMPFuStN3dQ4U7v0xa3dC2pGWQR7ZobQWeDIc2VRkyvRX67InrYipTkJupboWFqjnivKTvB4/EMGUqN1Ap05EpuTzNXh99oA7b96IpwSAPfjZi7VrfLydT7dxOvV3ZSSeXF2vLg5NiKpfncanL/vurYuRfZHryLdcZ9kjPlGilDWEkqU9BO725obNOyzA1vORfsRUqkfSVVN95sHyFRya1fkgM05TamZUKePJj2sO/UZD765JdPaSgaq9Pudm1wQOeXHvv4QkzDENTj+qqd78aqcNqvlemv/meQK8/IL+3Vu7yd1XT7xzVdx/W0qqjlaVlkBuB+lYL8ntO7Sepn3bqTzFdf8uL61X79UrNC9woo746uZVDwpiScs0Kme72MV3vdjnll1MBw8URt2hSldevdjm1wfHxOA54uvDYA6VjD5T0q5iuv2/lN1rzwZt6JuOPMupKLdUV9krL5Wet2SKPl8flULmfLtdUYyrYIo89yIN/9QJON3/OaFJNvV85qpWZGdtEN6ty3S5t9wfv4fCWJ/VeSI60DHLDXyezlcbI4+V2OVXuC+3GxJK1lNLOrAp2rcfA7QwFuYMgx+58/oC8flPZqpEZ40Q3q3I9LpUrOEnOqC1L6r2QHOnZte6vb5UNYazwZDhUEaBFHq2yzqdXvtyuzttXKaduW1LvVZjnVoceXaXO4yQjjp9zTTPOrvXQgTpOtxzMWscuqrx+SVKWWRPzRDerct0uVckj03DKoEWektIyyBVonQ1hrPC4HKo1CfJoz31aovkvf6SPPDNb54YfSK7TlsnXeVDMRUxJOWalAu4YW+SNTsbjzxmNVdcHg9wTqIl5DblVuR6XJEP1Gbly1NEiT0XpF+SmKcnRKhvCWOEOrTuXJIOudUlSaU29OhgVkqQfBl+n8h4nJOU+D7zxneq2f6O/118rZ/n3cQW5EfArx6xWVbwtcsMtN0GOXYRb5O5AtcyMgqTeK88djAGvK08ugjwlpV+QG4bKTl6knIOOkuI74rdVeFhfvJvKOr8KMoJLtLI7HSRXYY+k3KfWU6MvQpODHZVb4iqbbVZKUuxj5KEgr2eMHE2oDgV5ZqA66ZPd2oWCvNbZTnkEeUpKy8lu9V2PlbI72l2NJnkyHPLKJdNwMNktpLLOp06u8MYYuUm7j2FItXLLzOooZ8XmuMq2CwR7DOJZfiZJ9Qbb92J31V6fJCnDV90qk90kqcbRTkYdY+SpKC2DvC0LttQMBRweWmohlV6/9g+1yAOZeUm7j6HgWLfyuslRGV+Q5wSCLXIzxjFyT9RZ9axdTdtCAAAgAElEQVQjx67CLXKnr6pVlp9JUqWjnQxa5CmJIG9jwkef+p0exshDKut86hhukbuT1yKXYcg0JbN9t7hb5OEgj7dr3atMfmDDbqq8fjnllzNQl/QWeYbTIY/LoUrlyEGLPCUR5G1Mw7IkD12uIZV1PnVwBn+oSWrXuoItcjOvW9xj5O3M+LrWXQ5DDkOqMwhy7K7a61eOwp/55E/MzfO4VGbmsPwsRRHkbYwnalmSODFNUug4R6MVxsglmaYpte8qh7cirvHCSIs8xq51wzDkdjmCR9wS5NhFdb1f7RT6zCd5+ZkUnPBWamYF9/2nJzDlEORtTGQSlMNN13pIZZ1PeUa1TFd2Uk+DC29nbeZ1k6S4xsnbmeEx8tha5FLUUkN6XrCLaq9fuaEjTFsjyPM8Lu0IBHd3o3s99RDkbUy4RV5vsCwprLLOp1yjWoFkjo9LMmQEJ7u1DwZ5POPkuWaFag2PFMeOge7Q5j/8OWNX1V6/9s8Mro9trRb5dl/whDUmvKUegryNiUyCMpjsJkleX3DP6XaqlpnEGeuSJCO4X1CkRR5HkOeYlao04vtBw+1yqMbMCJ7GF/DHVRb7tqp6vzo6Qy3yVhoj/zES5LTIU036bQjTxoVnrXuNTBm+CptrY7/K0Hra7EC1TE+yW+Sh5WftCmU6MpSz+m5lrYvtDPmh3s360VUUOnoiNsEgD53C56uVkrzMCKmj2utXd5dX8kuBJM9al4JL0DZ6PZIhtmlNQQR5GxNukdcZbiadKLirmyRlBapkuvdP6r0MIzTZzXCo6mdXyfXjupjLvltZpE+zf64pcdzP43Ko2hfsijf8tTJFkLd1VV6f1q3fJtem1crwVSbtPl1/KtEArZekpK8jl4JBvtXrkdx0racigryNcToMZTiN4GxmJkGpsi7YInf7KxXI7JnUexkyIv9fc9Rv4yp7+6IPtF9OZlxB7nY5VO0NBbmvNtgbgDbtobe+13vvv6Hn3Vcn9T5DQv81HS6ZnuTvQpnrcanMDB1lSpCnHIK8DXKH9ls3WH4WCfJMf6UCSVx6JoVb5NbKWinndjlVFWgIcrR9G0trNCh3p+SVPhl0h2pyDkjavbq0d8udVyDTneS5IQq2yMtDPUIO1pKnHIK8DfK4nKoxaZFLwe1ZJcnlq1RdK/yDZrVVbKWc2+VQZSjI2aY1NZRU1Gl0VoXklQoHnCTT0yGp9wsk9d0b5LpdqpdLdYZbpWue0PefNj+s5DCkXvvnyBhwotQlOScSIjYEeRvkdjlUo9DyM9NsWOCchirrfMqQT05/XdJnrUc2hLHANE3F+6cUDPLgX0F+aEsN2yq96p6/U6bTHfO++qngkIJ2OrBjll6tGawB3s91kPe9Zsv4TVN5lZVyln8s/ZIgtxNB3gZ5MhyqDrfU/LWSK8veCtmoss6nXFVLUit0rVv/gcm0UN7tcqjST9d6qqj3B7Sjyqui/J/kb9d5n/oBu0t7j5acO1jSf2IuM+reN/Svjo/qmOo3k1cxxIR15G2Q2+VUtcmZ5FJ4V7cqSUk+MEXBrsKWTDiz0iKv8Ida5Gn+55wKfqz0ypTU0b9dgXZd7K6O7fKzXNoRaCfV7JDM1hoEQFMI8jbI43KoKhzkaT7hrbLOr4KM8MYYrTBG3oLJbvE20Nwupyp8oU4xlhq2edsqgp/DPG+JAu0621wb+7XPytC2QK4MM8BMd5sR5G2Q2+VomM2c5mOnlXU+dXIl/8AUKdg1blpsk1sp54ma7Jbuf86poKSiTg4F5K7ZJj8tcnXIzlCJLzTTvWaHzbVJbwR5G+TJcEbGTtP9BLRKr1/7h1rkgVaZ7GatbLBFHv8YeS1DKCljW2Wd9leZDNNHi1xSflaGttSH1p7XEuR2YrJbG+RJgdnMlXU+rdtSpg47PpTLX5W0+3T5qUQDjdAOV8k+NKUFc5dMWRsjrxVBnipKKurUM7NUkhTIIcjzszL0RV2O5KRFbjfbgnzlypW69dZbFQgE9Itf/EIXXnihXVVpc9wuh8r98Y+Rb9hRrbXflajn9pfkDNTHVCY/K0MHFuRLR58RVx3vW/mtNq57RUvcs+MqF6+GHa4yWmWHK6stclkaI28IctaRt30lFXXqk1Um1YmudQX/7fihPhTktT/ZXZ20ZkuQ+/1+zZ49W//6179UWFioKVOmaNSoUerVq5cd1WlzPBlObfU7JSO+FvmfV3ypQ35YqvMzFsR9T7+zVuo9Lebr3/lup2Z1+FJmtUNrjvuXAo7MuO8Zq+AOV4VJPwUqeIyp9THyuFvkGQ7VieVnLWWapqq8fhn11ZLpS9p9yst2qE/Gj1Kd6FpXMMh3KNhLZtAit5UtQb527Vr16NFD3bt3lyRNmDBBxcXFBHmI2+VQmS9Dyoi9RR4wTX2xrVKz9iuRvyZXX538fLNNxI07a3TN8s/1Qse7lLf+/2IO8h/Ka7W5rFZDOn0qX0F/dT9idEzlWqI1Fre0fIvWeMfInZIM+R3uNjuEkgpuemG9Kj57UQsz7pTDSN6O9U+F/mtmZCd9R7dUkJ+VoVq55XdlyVG70+7qpDVbgrykpERFRUWR3xcWFmrt2rV2VKVNip7NrBj/gd9SVqsqr1+9zQ3yd+qrjkUHNlumQ4EprSzTG85jdNL3T8moK5Ppbt9sufe+L5VHdepc9alqe10QU/1SQeQYUwuCG8LEVyZ80l3A6aZFbpHPH9CrX27XnLwPVV+frXe6Jffz2Gv/HBX0PnKf2gzGqvys4L9R9Zkd5Kiha91OtgR5U9tg7m3Gr9NpKD8/npOem+d0OhL+nomSn+tRremWJOVk+JUVQz1Xb6mQoYA61Xwt9Tk75u9tbN9CLfqgvyY4H1f+9jdlHn56s2XWlVTq+Kyv5QjUK7PPSGW00ecYL48n+A+Tlc+Gw2EoM9MZV7n984O9LQFXltwOn1xt9Dm25b8r727YoSqvT8NyPlJGr5EaMuWGpN/T4XQo388GKAfUB59BbUa+8nxlbfYz0trs+PtiS5AXFRVp69atkd+XlJSooKBgj9f7/aZKS6sTWof8/OyEv2eimD6/akKToGoqylUTQz0/3PCTDnKUyOmrVkXuIaqN8Xv7Wbc8PfbOQdruaK/2Sy+Wd+nlzZa5WZLb8Mt0uLQzd4DURp9jvOrqfDJNU35/IO7Pht8fkK/eH1e5+trghMR6I1NmdaUq2uhzbMt/V176+Acd4tiirJqtqigaFvPnviXa8vNoTc764IFGFc72yqn4kWcSksjPR6dOsa3UsSXI+/fvrw0bNmjjxo0qLCzU8uXLdffdd9tRlTap8bKk2MbI12+r0nHttkpeybd/35jvNfiADrpoaE+tKL1W3UrfjamMIannfjlq132AlJkT873aOqMFW7RaPf1MkmrNTLnKS7T5qzXNljEU3BfbaFcoMyv5s/itqqj1qaS8SlkVG5K6fefGL7/V+XnvSbWS94ARSbsPdpfrccmQVOFory61m+2uTlqzJchdLpeuv/56XXDBBfL7/Tr99NPVu3dvO6rSJnlcDvnkUsBwyVe1U1UVzc8I3bytRFOzvpNZ75Sv4yEx38vpMHTezw9Qfv6hKi2dGlc96+K6uu1r7Q1hct3Bv35fV2XoZzVvaeALk2Iu688u0I5zVkuOtrcVRMA0Nf2xNTq1fJFmZTyZ1Hs9GPqvr0MvBfK6J/VeaMzpMJTncWmnctvsrPXtVV7NenKdppbN0xTj5aTeq8rI1o8nL1F+/pFJvU9TbPtXYMSIERoxgp+gm5KTGfxjKQ+4lf/xArX/uPnlZC9JUr3k69hHcnmSW8F9VEtb5PFOfyrK8+iBM47Qtp/+olUVn8VU5o1vdqiwer2mVz8lV8ka+ToPjruuyfb+xlJt2lml83NX6secI/XFAfH9gBgPhwz1LsiRo7B/0u6BPeuQnaHtgVw56islf53kdDdbpry2Xv96+3sdtu1/6ln7Scz38rgcyvDkqCzrAPkdzd9HktZsLtO0qi/1K8eL+jh3uHZmFjVfyCIzI1c9OhQm7f33pu39OA8NPiBfV4/ppVe236b9ar6JqYzDMHRoYTs5u/88ybXbdxlSi5rkViYyH909X+p+pKTYfor/pP0PunvFRzon62m5NxS3ySB/5uMSjXV/orz6H1X281vU5+AJSb8nU8/skZ+VofXlwWHAr568RvWO5o9c/vqnKg3zbtAJjtX6SfmqjzGGAqapXFUr14h9k6zhkuSQag89Q4Wj7lbhPrragCBvgzJdDp02oIuk5meQRzMlJW87jDRgGK3aIrfi2AM76M/K1qZ2R6jzdy+rasgfYypX5wvo36s3av8fitW/Kr7zo51OQ35/7E9mTFmtjvVsUMDRQd4Dx8Z1L6SWfp3z9OaWzqrNzNCQH2M7y3y4pIArQ1VHX6nA4JlyGrEd+RHwB/R9lVfO2p9kxLjxT1aGU9nuTJk5e55MvS8gyIGQlo6Rt8ba4qI8j3p3ytHy2v66uOLfWr/kavmN5v8abyqt0cFVW/VL16sqVa5qFVvXpBU9nVK+K0PVAy+VnMnb8Q/2u2JET+Wf0k+lpeerIsn3ynA6VJDnkfK6xlUueVsEtR0EORDS4g1hEliXvZl4eKEWv3akfpW5REO3PRJzuYDLoZrDz1b9cbPljCNgrSynKY/ragAtQZADIS06/cyMf691q846upvOOvoXqtUvxH5wADiPHAgxQlHc1M6DMZXfN+fRAGjjCHIgLBTEVnI82LVOkgNofQQ5EBKOYSvtcbM1B8kBIApBDoSEu8YDFprk5DgAuxDkQEjDGHn8ZU2LG8IAQEsR5EBIOIitLkFjjByAHQhyYFdWutZNZq0DsAdBDoS0aLJbIisCAHEgyIGQ8DGklsfIE1wfAIgFQQ6ENLTIrW4IQ5QDaH0EORBitHhDGABofQQ5sAurG8LQIAdgB4IcCGnRGDnT3QDYhCAHQhoa1FaXn9EkB9D6CHIgpCVj5BJj5ADsQZADu2CMHEAqIciBkIYxciuHpjBGDsAeBDkQ0tJjTNlrHYAdCHIgJBLkVteRk+MAbECQAyEtP/0MAFofQQ5EWJ+2znnkAOxCkAMhLWmRB8uQ5ABaH0EOhLRojJzlZwBsQpADIQ0tcivLz2iPA7AHQQ6EtKRFLsbIAdiEIAfCwhvCWChKixyAXQhyIKRFLXKJQXIAtiDIgZAWn36WyMoAQIwIciCkJaef0bUOwC4EORAS3iudnnUAqYQgB8IiLfL4ojx8PYemALADQQ6EWD39LHI9OQ7ABgQ5EGJ1jDx8PTkOwA4EORBidYw8fD1j5ADsQJADIYbVheSMkQOwEUEO7IIWOYBUQpADIS0dIwcAOxDkQEhkjDze5WeR8gDQ+ghyIMTy8rPwGDl96wBsQJADIS3ZolWiRQ7AHgQ5sAsmuwFIJQQ5EGJYbJIz2Q2AnQhyIMT6Fq2MkQOwD0EOhLBFK4BURJADIVZb5ABgJ4IcCDMsriMPt8hpkgOwAUEOhLR0jBwA7ECQAyEtPDOFyW4AbEGQAyENOcwWrQBSB0EORITHyOMsxqx1ADYiyIGQyPKzOMs1rCNPbH0AIBYEORBieYx8t3cAgNZDkAMhDS1ylp8BSB0EORBiWBwjZ7IbADsR5ECYxTFymYyRA7APQQ6ENKw+Y/kZgNRBkAMhlmetcyA5ABsR5EBIS2etE+MA7ECQAyGRyW5xlgsfskKQA7ADQQ6ERc4jt3YICj3rAOxAkAMh1k8/C5cnyQG0PoIcCIlMdrN4+hk5DsAOBDkQYlhsk0f2Wk9wfQAgFgQ5ENLSFjlj5ADsQJADu7A21Y0xcgD2IMiBEFrkAFIRQQ6ENKwjt7ZFKwDYgSAHQizv7MahKQBsRJADIZb3Wg+XZ4wcgA0IcmBXFjdbJ8YB2IEgB0IMw+Je65HyCa0OAMSEIAdCrJ9+xnQ3APYhyIGQlp5HbtAkB2ADghwIaWiRW1t+RowDsANBDoRZHCMXG8IAsFGLgvz555/XhAkTdOihh2rdunWNvjZv3jyNHTtW48eP1+uvvx55feXKlRo/frzGjh2r+fPnt+T2QEK1dIycHAdghxYF+SGHHKL77rtPgwcPbvT6V199peXLl2v58uVasGCBbrrpJvn9fvn9fs2ePVsLFizQ8uXL9eyzz+qrr75q0TcAJIph8UByk2nrAGzkaknhgw8+uMnXi4uLNWHCBGVmZqp79+7q0aOH1q5dK0nq0aOHunfvLkmaMGGCiouL1atXr5ZUA0iIhhxnjBxA6kjKGHlJSYmKiooivy8sLFRJSckeXwfagshe6xYXkhPkAOzQbIt8+vTp2r59+26vz5w5U2PGjGmyTFOzfg3DUCAQaPL15jidhvLzs5u9Lh5OpyPh75nKeB5Sbo1PkmQ44vu8tauuD/63nXuffIZ8NhrjeTTG82jMjufRbJAvXLgw7jctKirS1q1bI78vKSlRQUGBJO3x9b3x+02VllbHXY+9yc/PTvh7pjKeh1RZUStJ8vsDcT2L8lC5qirvPvkM+Ww0xvNojOfRWCKfR6dOuTFdl5Su9VGjRmn58uXyer3auHGjNmzYoCOOOEL9+/fXhg0btHHjRnm9Xi1fvlyjRo1KRhWAuLV8Q5iEVgcAYtKiyW4rVqzQzTffrB07duiiiy7SYYcdpoceeki9e/fWiSeeqJNOOklOp1PXX3+9nE6nJOn666/XBRdcIL/fr9NPP129e/dOyDcCtJTVMXImuwGwU4uCfOzYsRo7dmyTX7vkkkt0ySWX7Pb6iBEjNGLEiJbcFkgOq0nMeeQAbMTObkBIy7doJckBtD6CHAhp6Rg5OQ7ADgQ5EMIYOYBURJADIZZ3dmOMHICNCHIgLNy1HvfxZ42KA0CrIsiBEItnpjSsIyfKAdiAIAdCDIuz3SKXk+MAbECQAyHWTz/jPHIA9iHIgRCjpWPkJDkAGxDkwC4YIweQSghyIKShRW6tSU6LHIAdCHIgJLIhTJzlrHbFA0AiEORAiNUx8shkN1rkAGxAkAMhLTz8jDFyALYgyIFdWD/9DABaH0EOhIQ3hIl7yDvcIifJAdiAIAdCGs4jj69cvBvIAEAiEeRASMMOrRa71mmSA7ABQQ6EWG6RRya7AUDrI8iBMItj5A0t8oTWBgBiQpADIZEctpjk5DgAOxDkQIj1MXKmrQOwD0EOhDBGDiAVEeRAiOW91sPlSXIANiDIgTCLp5/RIgdgJ4IcCIl0rcddMnRoClEOwAYEORBi+fQzNnYDYCOCHAix2qKO5DgNcgA2IMiBEMPqGHm4fGKrAwAxIciBXcR/+llojJwkB2ADghwIsTxGHi5PmxyADQhyICQcxAGLy8/IcQB2IMiBkJa3yAGg9RHkQIjVIDYZIwdgI4Ic2EW8s9bDGCMHYAeCHAgxrJ5HzhatAGxEkAMhlk8/2/UNAKAVEeRASMN55PExI3utA0DrI8iBkIYWucXTz5jtBsAGBDkQZnGMPFI8cTUBgJgR5EBIJIgtnn5GgxyAHQhyIKRhjDzeQ1M4xxSAfQhyIMTyrHVa5ABsRJADIZbXkYfLM0oOwAYEObCLuDd2o0UOwEYEORDFkPUxcnIcgB0IciCKEUzyuFjcmh0AEoIgB6JYyPGGMXL61gHYgCAHohlG/Du7hYsmvjYA0CyCHIhipUUuziMHYCOCHIhiGNZPPyPHAdiBIAeiWBojbxgkT3BtAKB5BDkQxWCMHECKIciBFmKLVgB2IsiBKIasrAtnQxgA9iHIgSiGYWFnt3CLnCgHYAOCHIhiyLA8a50cB2AHghyIEmyRx4fJbgDsRJADu4j/PHI2hAFgH4IciGJljDxSljY5ABsQ5EAUQ/H3rbP8DICdCHIgSkvGyAHADgQ5EMXKOnLGyAHYiSAHohiGwRg5gJRCkANRrLXIQ2XJcQA2IMiBKKwjB5BqCHJgF3Gffhb/5uwAkDAEORDFsNA/3nAcOW1yAK2PIAeiWDv9rKEsALQ2ghyIYmmMnMluAGxEkANRgi3yOMfIOY8cgI0IcmAXVlvkNMkB2IEgB6IYhvXzyIlxAHYgyIEohlpw+hlJDsAGBDkQxQgmeXzCk90SXRkAiAFBDkSxtEVreLIbTXIANiDIgWiGYX2yGwDYgCAHolhbfka3OgD7EORAFKuHptCrDsAuBDkQxdIWraZJixyAbVoU5HfccYdOOOEETZo0SZdeeqnKy8sjX5s3b57Gjh2r8ePH6/XXX4+8vnLlSo0fP15jx47V/PnzW3J7IOEMw4h7+ZkZLJiU+gBAc1oU5EOHDtWzzz6rZ555RgceeKDmzZsnSfrqq6+0fPlyLV++XAsWLNBNN90kv98vv9+v2bNna8GCBVq+fLmeffZZffXVVwn5RoCEiXfWuskYOQD7tCjIhw0bJpfLJUkaOHCgtm7dKkkqLi7WhAkTlJmZqe7du6tHjx5au3at1q5dqx49eqh79+7KzMzUhAkTVFxc3PLvAkgQq8vIaZADsEvCxsiffPJJDR8+XJJUUlKioqKiyNcKCwtVUlKyx9eBtsIwLKwjp0UOwEau5i6YPn26tm/fvtvrM2fO1JgxYyRJ//jHP+R0OnXyySdLanr5jmEYCgQCTb7eHKfTUH5+drPXxcPpdCT8PVMZzyPI6XRIhuJ6Fm63S4aR+M9oW8FnozGeR2M8j8bseB7NBvnChQv3+vWlS5fq1Vdf1cKFCyOhXFRUFOlml4It9IKCAkna4+t74/ebKi2tbva6eOTnZyf8PVMZzyMoEDAVCMT3eautrZekffb58dlojOfRGM+jsUQ+j06dcmO6rkVd6ytXrtSDDz6of/zjH8rKyoq8PmrUKC1fvlxer1cbN27Uhg0bdMQRR6h///7asGGDNm7cKK/Xq+XLl2vUqFEtqQKQUJbHyJNQFwCIRbMt8r25+eab5fV6de6550qSBgwYoNmzZ6t379468cQTddJJJ8npdOr666+X0+mUJF1//fW64IIL5Pf7dfrpp6t3794t/y6ABLE8Rk6SA7BJi4J8xYoVe/zaJZdcoksuuWS310eMGKERI0a05LZA0hgW2uSmzFA5AGh97OwGRLHSIg+XAwA7EOTALjj9DEAqIciBKFZPPwMAuxDkQBTD0nnkJl3rAGxDkANRLJ1+JjHZDYBtCHIgiqXzyFl+BsBGBDmwKwtj5OQ4ALsQ5EAUq2PkAGAXghyIYmWMPHiMKW1yAPYgyIEowTHy+FvYxDgAuxDkQBRLLXImuwGwEUEONGJhjJwtYQDYiCAHohgWzjENtshpkgOwB0EORAnmOMvPAKQOghyIYun0M8bIAdiIIAeiWOhZD51HDgD2IMiBaIYR/+lnzHUDYCOCHIhirUXOZDcA9iHIgShWxsiZ7AbATgQ5EMVSIHMeOQAbEeRANCtj5KJFDsA+BDkQxdIYeXCQPAm1AYDmEeRAFMunnyWjMgAQA4IciGLl9DOTMXIANiLIgSiGDEvrwslxAHYhyIFohrUxctaRA7ALQQ5EMaT4Tz9LRkUAIEYEORDF2hg5XesA7EOQA1GsjZEz2Q2AfQhyIIphZYxcwR8AAMAOBDmwi7jXkbOQHICNCHIgSnBDGLZoBZA6CHIgimEYFpafMUYOwD4EORDFyvKzYDmSHIA9CHIgiuXlZ+Q4AJsQ5MAurByaAgB2IciBKJbHyJNSGwBoHkEORLF8jCl96wBsQpADUayMkUssPwNgH4IciGKpRc5kNwA2IsiBRuJPZCsteABIFIIciGIYFnZ24zxyADYiyIEohqwemgIA9iDIgSjBFnmchRgjB2AjghzYRfw5zjpyAPYhyIEohmFYGiOncx2AXQhyIIrlMXJyHIBNCHIgimEhyZnsBsBOBDkQJZjj8e8IQ4scgF0IciCaYVjbaz0plQGA5hHkQBQrY+TBgkQ5AHsQ5EAUy3utJ6U2ANA8ghyIYuX0M2atA7ATQQ5EMWRYWH/GhjAA7EOQA9EMKWBhshtNcgB2IciBKFaWnzFGDsBOBDkQxcqhKSw/A2AnghyIYsiwMkROzzoA2xDkQJRgizz+3dYN2uQAbEKQAy3E4WcA7ESQA1HYEAZAqiHIgSiGYWGMXIyRA7APQQ5ECbbI411+xhg5APsQ5EAUw8LGbuFyAGAHghzYhZUxcgCwC0EORAmOkVs4NCU51QGAZhHkQBRDirtv3TRNGfStA7AJQQ5EsTJGToscgJ0IcmAXjHkDSCUEORAluNe6hdPPaJIDsAlBDkSxdvoZTXgA9iHIgSjB88jjE2yR0yQHYA+CHIhi5fQzJrsBsBNBDjRiIZIZIwdgI4IciGJ1jJwcB2AXghyIYnWMnM51AHYhyIEolsfIyXEANiHIgShWWuThcgBgB4IciGYYFk4/M2mRA7ANQQ5EsZLHbAcDwE4tCvI5c+Zo0qRJOuWUU3TeeeeppKREUrCFcsstt2js2LGaNGmSPvnkk0iZpUuXaty4cRo3bpyWLl3astoDCRYO8njGydkQBoCdWhTkF1xwgZ555hk9/fTTOv744zV37lxJ0sqVK7Vhwwa9+OKLuvnmm3XjjTdKkkpLS3X//ffr8ccf15IlS3T//ferrKysxd8EkCjhPI6nlc2GMADs1KIgb9euXeT/a2pqIq2S4uJiTZ48WYZhaODAgSovL9e2bdu0atUqDR06VPn5+Wrfvr2GDh2q119/vWXfAZBARiiS4xonZ4wcgI1cLX2De+65R8uWLVNubq7+/e9/S5JKSkpUVFQUuaaoqEglJSW7vV5YWBjpjt8bp9NQfn52S6u6y3s6Ev6eqYznEeTJypAktW+fJZcztp9zDYchd6Zrn31+fDYa43k0xvNozI7n0WyQT58+Xdu3b9/t9ZkzZ2rMmDGaNWuWZs2apXnz5mnRokWaMWNGk+OLhmHs8fXm+P2mSkurm70uHvn52Ql/z1TG8wiqq62XJJWWVscc5H6/KW+9f599fnw2GuN5NMbzaCyRz80OypQAAAqiSURBVKNTp9yYrms2yBcuXBjTG02cOFEXXXSRZsyYoaKiIm3dujXyta1bt6qgoEBFRUVavXp15PWSkhIdc8wxMb0/0BoYIweQalo0Rr5hw4bI/7/88svq2bOnJGnUqFFatmyZTNPUhx9+qNzcXBUUFGjYsGFatWqVysrKVFZWplWrVmnYsGEt+gaARLIyRm6a7LUOwD4tGiO/++679e2338owDHXt2lU33XSTJGnEiBF67bXXNHbsWGVlZem2226TJOXn5+u3v/2tpkyZIkm69NJLlZ+f38JvAUgcKy3y6HIA0NpaFOT33Xdfk68bhqEbbrihya9NmTIlEuRAWxXXOnI1tOQBoLWxsxsQxdLObgySA7ARQQ5ECa+iYLIbgFRBkANRGrZojb0Mh6YAsBNBDkRpmOwW33Q3xsgB2IUgB5oQX4ucWesA7EOQA1GsnGLGMaYA7ESQA1Esj5EnpTYA0DyCHIhieYycvnUANiHIgSbE1SIXy88A2IcgB6JYWkfOZDcANiLIgSiRPI6rRc50NwD2IciBKJHJbnGEc7BFTpMcgD0IciCK5dPPEl4TAIgNQQ40YuU8csbIAdiHIAeiWGmRM0YOwE4EORClYbJbnGPkdK4DsAlBDkSx1iKnax2AfQhyIIqVLVqjywFAayPIgWiWNoThPHIA9iHIgSgNLfI4xsgblQSA1kWQA1GsxDHLzwDYiSAHolie7JaMygBADAhyIIphYUMYMUYOwEYEORDNwnnktMgB2IkgB6JYWX5mspAcgI0IciCKlTymRQ7ATgQ5EMXSGLkIcgD2IciBKJaPMSXJAdiEIAeaEOuGMOHrODQFgF0IciBKvC3yyHXkOACbEORAFEPxJXm44U6OA7ALQQ5EiSw/i/H68HWMkQOwC0EORDHi3RCGMXIANiPIgSbEuvyMFjkAuxHkQBQjzvPI411vDgCJRpADUSIN63hb5EmoCwDEgiAHosQ7Rh5ZR07fOgCbEORAFCuHpkSXA4DWRpAD0eIdI29cDABaHUEORIl7jJzJbgBsRpADURo2hIlxjFyMkQOwF0EORIl7r3W2aAVgM4IcaMTieeQkOQCbEORAFKstcgCwC0EORGmY7BbfGDkA2MVldwWAtiTcIr/rla/Vzt38Xw9fgMluAOxFkANRDunUTkN6dlRFTb0q63wxlRnYNU9Hds1Lcs0AoGkEORClINetf597jEpLq+2uCgDEhDFyAABSGEEOAEAKI8gBAEhhBDkAACmMIAcAIIUR5AAApDCCHACAFEaQAwCQwghyAABSGEEOAEAKI8gBAEhhBDkAACmMIAcAIIUR5AAApDCCHACAFEaQAwCQwghyAABSGEEOAEAKM0zTNO2uBAAAsIYWOQAAKYwgBwAghRHkAACkMIIcAIAURpADAJDCCHIAAFKYy+4KJMuoUaOUk5Mjh8Mhp9Opp556SqWlpZo1a5Y2b96srl27as6cOWrfvr1M09Stt96q1157TR6PR7fffrsOP/xwu7+FhGrqedx33316/PHH1bFjR0nSlVdeqREjRkiS5s2bpyeeeEIOh0PXXnutjjvuODurn3Dl5eW69tprtX79ehmGodtuu00HHXRQ2n4+mnoeq1atSsvPxzfffKNZs2ZFfr9x40bNmDFDkydPTrvPx56eRUVFRVp+NiRp4cKFWrJkiQzD0CGHHKI///nP2rZtm6688kqVlZWpb9++uvPOO5WZmSmv16vf//73+uSTT5Sfn6977rlH3bp1S3ylzH3UyJEjzZ9++qnRa3fccYc5b9480zRNc968eeadd95pmqZpvvrqq+b5559vBgIBc82aNeaUKVNavb7J1tTzuPfee80FCxbsdu2XX35pTpo0yayrqzO///57c/To0abP52utqraK3//+9+bjjz9umqZp1tXVmWVlZWn9+WjqeaTz5yPM5/OZxx57rLlp06a0/nyYZuNnka6fja1bt5ojR440a2pqTNM0zRkzZphPPvmkOWPGDPPZZ581TdM0r7vuOvPRRx81TdM0Fy1aZF533XWmaZrms88+a15xxRVJqVdada0XFxdr8uTJkqTJkyfrpZdeavS6YRgaOHCgysvLtW3bNjuraqvi4mJNmDBBmZmZ6t69u3r06KG1a9faXa2Eqays1LvvvqspU6ZIkjIzM5WXl5e2n489PY892dc/H9Heeustde/eXV27dk3bz0dY9LPYk3T4bPj9ftXW1srn86m2tladOnXS22+/rfHjx0uSTj31VBUXF0uSXn75ZZ166qmSpPHjx+utt96SmYQ92PbpID///PN12mmn6b///a8k6aefflJBQYEkqaCgQDt27JAklZSUqKioKFKuqKhIJSUlrV/hJNv1eUjSo48+qkmTJunqq69WWVmZpN2fR2Fh4T71PDZu3KiOHTvq6quv1uTJk3XNNdeouro6bT8fe3oeUnp+PqItX75cEydOlMS/H9HPQkrPz0ZhYaHOO+88jRw5UsOGDVO7du10+OGHKy8vTy5XcKQ6+s+/pKREnTt3liS5XC7l5uZq586dCa/XPhvkixcv1tKlS/Xggw/q0Ucf1bvvvrvHa5v6CckwjGRWr9U19TymTp2qFStW6Omnn1ZBQYFuv/12Sfv+8/D5fPr00081depULVu2TFlZWZo/f/4er0/X55Gun48wr9erl19+WSeccMJer0uH57Hrs0jXz0ZZWZmKi/9/e/fuklwcx3H8TVbQIEka/bo4lYNENdYYRhHU2h8RLTW41CZdliBq6a8IXQwEXZRq7AbZFIGD2hJd6NCFzjNIh+fB2uoJz/m8JhWFcz5+ON/juWCOXC5HoVDAsizy+Xzd+z7W+X/l4dpB3tXVBUAwGGRycpKzszOCwaBzyOvm5sa5UMMYQ6VScT5bqVScPW+3+CyPUCiEz+ejqamJubk5zs/Pgfo8qtWqq/IwxmCMYWRkBIDp6WkuLi4824+v8vBqPz7k83kGBwcJhUIAnu0H1Gfh1W4cHh7S19dHR0cHLS0tTE1NcXx8zP39PW9vb8C/378xhnK5DNR2mB8eHggEAt++XK4c5E9PTzw+PjqPDw4OiEQixGIxUqkUAKlUiomJCQDnddu2OTk5we/3u6p8X+Xx93m8bDZLJBIBanmk02leXl4olUpcX18zPDz8K8v+Ezo7OzHGcHV1BdTO/fX393u2H1/l4dV+fEin08zMzDjPvdoPqM/Cq93o6enh9PQUy7KwbZujoyMGBgYYHR0lk8kAkEwmicViQC2PZDIJQCaTYWxs7Ed+kbvy389KpRILCwtA7cKE2dlZ5ufnub29ZXFxkXK5THd3N9vb2wQCAWzbJpFIUCgUaGtrY319naGhoV9ei+/zVR7xeJzLy0sAent7SSQSzgZod3eXvb09fD4fy8vLzq0lblEsFllZWeH19ZVwOMzGxgbv7++e7Ad8nsfq6qpn+2FZFuPj42SzWfx+P4Bntx+fZeHlbcfOzg77+/s0NzcTjUZZW1ujWq2ytLTE3d0d0WiUzc1NWltbeX5+Jh6PUywWaW9vZ2tri3A4/O3L5MpBLiIi4hWuPLQuIiLiFRrkIiIiDUyDXEREpIFpkIuIiDQwDXIREZEGpkEuIiLSwDTIRUREGpgGuYiISAP7A0EzH5tpPPlIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data[\"EVBOA_MIN\"][500:800], label = \"EVBOA_MIN_With_Outliers\")\n",
    "plt.plot(df[\"EVBOA_MIN\"][500:800], label = \"EVBOA_MIN_Without_Outliers\")\n",
    "plt.legend()\n",
    "plt.title(\"EVBOA_MIN\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "444"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "74*6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data[\"EVBOA_MAX\"], label = \"EVBOA_MAX_With_Outliers\")\n",
    "plt.plot(df[\"EVBOA_MAX\"], label = \"EVBOA_MAX_Without_Outliers\")\n",
    "plt.legend()\n",
    "plt.title(\"EVBOA_MAX\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data[\"EVBOA_AVG\"], label = \"EVBOA_AVG_With_Outliers\")\n",
    "plt.plot(df[\"EVBOA_AVG\"], label = \"EVBOA_AVG_Without_Outliers\")\n",
    "plt.legend()\n",
    "plt.title(\"EVBOA_AVG\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.plot(data[\"EVBOV_MIN\"], label = \"EVBOV_MIN_With_Outliers\")\n",
    "plt.plot(df[\"EVBOV_MIN\"], label = \"EVBOV_MIN_Without_Outliers\")\n",
    "plt.legend()\n",
    "plt.title(\"EVBOV_MIN\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data[\"EVBOV_MAX\"], label = \"EVBOV_MAX_With_Outliers\")\n",
    "plt.plot(df[\"EVBOV_MAX\"], label = \"EVBOV_MAX_Without_Outliers\")\n",
    "plt.legend()\n",
    "plt.title(\"EVBOV_MAX\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data[\"EVBOV_AVG\"], label = \"EVBOV_AVG_With_Outliers\")\n",
    "plt.plot(df[\"EVBOV_AVG\"], label = \"EVBOV_AVG_Without_Outliers\")\n",
    "plt.legend()\n",
    "plt.title(\"EVBOV_AVG\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data[\"EVPWA_MAX\"], label = \"EVPWA_MAX_With_Outliers\")\n",
    "plt.plot(df[\"EVPWA_MAX\"], label = \"EVPWA_MAX_Without_Outliers\")\n",
    "plt.legend()\n",
    "plt.title(\"EVPWA_MAX\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data[\"EVBMA_Latest\"], label = \"EVBMA_Latest_With_Outliers\")\n",
    "plt.plot(df[\"EVBMA_Latest\"], label = \"EVBMA_Latest_Without_Outliers\")\n",
    "plt.legend()\n",
    "plt.title(\"EVBMA_Latest\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data[\"EVIRP\"], label = \"EVIRP_With_Outliers\")\n",
    "plt.plot(df[\"EVIRP\"], label = \"EVIRP_Without_Outliers\")\n",
    "plt.legend()\n",
    "plt.title(\"EVIRP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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